Genetic algorithm - Knowledge

1638:(normal or natural adaptation, abbreviated NA to avoid confusion with GA) is intended for the maximisation of manufacturing yield of signal processing systems. It may also be used for ordinary parametric optimisation. It relies on a certain theorem valid for all regions of acceptability and all Gaussian distributions. The efficiency of NA relies on information theory and a certain theorem of efficiency. Its efficiency is defined as information divided by the work needed to get the information. Because NA maximises mean fitness rather than the fitness of the individual, the landscape is smoothed such that valleys between peaks may disappear. Therefore it has a certain "ambition" to avoid local peaks in the fitness landscape. NA is also good at climbing sharp crests by adaptation of the moment matrix, because NA may maximise the disorder ( 1672:(TS) is similar to simulated annealing in that both traverse the solution space by testing mutations of an individual solution. While simulated annealing generates only one mutated solution, tabu search generates many mutated solutions and moves to the solution with the lowest energy of those generated. In order to prevent cycling and encourage greater movement through the solution space, a tabu list is maintained of partial or complete solutions. It is forbidden to move to a solution that contains elements of the tabu list, which is updated as the solution traverses the solution space. 991:

disparate definition domains for the problem parameters. For instance, in problems of cascaded controller tuning, the internal loop controller structure can belong to a conventional regulator of three parameters, whereas the external loop could implement a linguistic controller (such as a fuzzy system) which has an inherently different description. This particular form of encoding requires a specialized crossover mechanism that recombines the chromosome by section, and it is a useful tool for the modelling and simulation of complex adaptive systems, especially evolution processes.

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plane . In order to make such problems tractable to evolutionary search, they must be broken down into the simplest representation possible. Hence we typically see evolutionary algorithms encoding designs for fan blades instead of engines, building shapes instead of detailed construction plans, and airfoils instead of whole aircraft designs. The second problem of complexity is the issue of how to protect parts that have evolved to represent good solutions from further destructive mutation, particularly when their fitness assessment requires them to combine well with other parts.

1666:(SA) is a related global optimization technique that traverses the search space by testing random mutations on an individual solution. A mutation that increases fitness is always accepted. A mutation that lowers fitness is accepted probabilistically based on the difference in fitness and a decreasing temperature parameter. In SA parlance, one speaks of seeking the lowest energy instead of the maximum fitness. SA can also be used within a standard GA algorithm by starting with a relatively high rate of mutation and decreasing it over time along a given schedule. 1581:(PSO) is a computational method for multi-parameter optimization which also uses population-based approach. A population (swarm) of candidate solutions (particles) moves in the search space, and the movement of the particles is influenced both by their own best known position and swarm's global best known position. Like genetic algorithms, the PSO method depends on information sharing among population members. In some problems the PSO is often more computationally efficient than the GAs, especially in unconstrained problems with continuous variables. 713:, for example, have been proposed in an attempt to provide an environment in which the hypothesis would hold. Although good results have been reported for some classes of problems, skepticism concerning the generality and/or practicality of the building-block hypothesis as an explanation for GAs' efficiency still remains. Indeed, there is a reasonable amount of work that attempts to understand its limitations from the perspective of estimation of distribution algorithms. 705:"Because highly fit schemata of low defining length and low order play such an important role in the action of genetic algorithms, we have already given them a special name: building blocks. Just as a child creates magnificent fortresses through the arrangement of simple blocks of wood, so does a genetic algorithm seek near optimal performance through the juxtaposition of short, low-order, high-performance schemata, or building blocks." 1378: 727: 278: 828:

generations, permitting other (less similar) individuals to be maintained in the population. This trick, however, may not be effective, depending on the landscape of the problem. Another possible technique would be to simply replace part of the population with randomly generated individuals, when most of the population is too similar to each other. Diversity is important in genetic algorithms (and

1447:(ES, see Rechenberg, 1994) evolve individuals by means of mutation and intermediate or discrete recombination. ES algorithms are designed particularly to solve problems in the real-value domain. They use self-adaptation to adjust control parameters of the search. De-randomization of self-adaptation has led to the contemporary Covariance Matrix Adaptation Evolution Strategy ( 587:"parents". New parents are selected for each new child, and the process continues until a new population of solutions of appropriate size is generated. Although reproduction methods that are based on the use of two parents are more "biology inspired", some research suggests that more than two "parents" generate higher quality chromosomes. 823:: certain problems may provide an easy ascent towards a global optimum, others may make it easier for the function to find the local optima. This problem may be alleviated by using a different fitness function, increasing the rate of mutation, or by using selection techniques that maintain a diverse population of solutions, although the 700:, and resampled to form strings of potentially higher fitness. In a way, by working with these particular schemata , we have reduced the complexity of our problem; instead of building high-performance strings by trying every conceivable combination, we construct better and better strings from the best partial solutions of past samplings. 867:), as there is no way to converge on the solution (no hill to climb). In these cases, a random search may find a solution as quickly as a GA. However, if the situation allows the success/failure trial to be repeated giving (possibly) different results, then the ratio of successes to failures provides a suitable fitness measure. 1532:

with respect to a distance measure, equal piles, etc., on which classic GAs proved to perform poorly. Making genes equivalent to groups implies chromosomes that are in general of variable length, and special genetic operators that manipulate whole groups of items. For bin packing in particular, a GGA

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of organisms with multiple loci controlling a measurable trait. From these beginnings, computer simulation of evolution by biologists became more common in the early 1960s, and the methods were described in books by Fraser and Burnell (1970) and Crosby (1973). Fraser's simulations included all of the

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Genetic algorithms with adaptive parameters (adaptive genetic algorithms, AGAs) is another significant and promising variant of genetic algorithms. The probabilities of crossover (pc) and mutation (pm) greatly determine the degree of solution accuracy and the convergence speed that genetic algorithms

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An expansion of the Genetic Algorithm accessible problem domain can be obtained through more complex encoding of the solution pools by concatenating several types of heterogenously encoded genes into one chromosome. This particular approach allows for solving optimization problems that require vastly

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These processes ultimately result in the next generation population of chromosomes that is different from the initial generation. Generally, the average fitness will have increased by this procedure for the population, since only the best organisms from the first generation are selected for breeding,

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one wants to maximize the total value of objects that can be put in a knapsack of some fixed capacity. A representation of a solution might be an array of bits, where each bit represents a different object, and the value of the bit (0 or 1) represents whether or not the object is in the knapsack. Not

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A number of variations have been developed to attempt to improve performance of GAs on problems with a high degree of fitness epistasis, i.e. where the fitness of a solution consists of interacting subsets of its variables. Such algorithms aim to learn (before exploiting) these beneficial phenotypic

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Genetic algorithms do not scale well with complexity. That is, where the number of elements which are exposed to mutation is large there is often an exponential increase in search space size. This makes it extremely difficult to use the technique on problems such as designing an engine, a house or a

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Genetic algorithms are simple to implement, but their behavior is difficult to understand. In particular, it is difficult to understand why these algorithms frequently succeed at generating solutions of high fitness when applied to practical problems. The building block hypothesis (BBH) consists of:

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published a series of papers in the 1960s that also adopted a population of solution to optimization problems, undergoing recombination, mutation, and selection. Bremermann's research also included the elements of modern genetic algorithms. Other noteworthy early pioneers include Richard Friedberg,

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implementations of genetic algorithms come in two flavors. Coarse-grained parallel genetic algorithms assume a population on each of the computer nodes and migration of individuals among the nodes. Fine-grained parallel genetic algorithms assume an individual on each processor node which acts with

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in the 1970s. This theory is not without support though, based on theoretical and experimental results (see below). The basic algorithm performs crossover and mutation at the bit level. Other variants treat the chromosome as a list of numbers which are indexes into an instruction table, nodes in a

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This means that the rules of genetic variation may have a different meaning in the natural case. For instance – provided that steps are stored in consecutive order – crossing over may sum a number of steps from maternal DNA adding a number of steps from paternal DNA and so on. This is

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Operating on dynamic data sets is difficult, as genomes begin to converge early on towards solutions which may no longer be valid for later data. Several methods have been proposed to remedy this by increasing genetic diversity somehow and preventing early convergence, either by increasing the

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Other approaches involve using arrays of real-valued numbers instead of bit strings to represent chromosomes. Results from the theory of schemata suggest that in general the smaller the alphabet, the better the performance, but it was initially surprising to researchers that good results were

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evaluations. In real world problems such as structural optimization problems, a single function evaluation may require several hours to several days of complete simulation. Typical optimization methods cannot deal with such types of problem. In this case, it may be necessary to forgo an exact

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For each new solution to be produced, a pair of "parent" solutions is selected for breeding from the pool selected previously. By producing a "child" solution using the above methods of crossover and mutation, a new solution is created which typically shares many of the characteristics of its

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Reactive search optimization (RSO) advocates the integration of sub-symbolic machine learning techniques into search heuristics for solving complex optimization problems. The word reactive hints at a ready response to events during the search through an internal online feedback loop for the

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proves that there is no general solution to this problem. A common technique to maintain diversity is to impose a "niche penalty", wherein, any group of individuals of sufficient similarity (niche radius) have a penalty added, which will reduce the representation of that group in subsequent

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and, more particularly, bacteriologic adaptation. Evolutionary ecology is the study of living organisms in the context of their environment, with the aim of discovering how they adapt. Its basic concept is that in a heterogeneous environment, there is not one individual that fits the whole

1463:(EDA) substitutes traditional reproduction operators by model-guided operators. Such models are learned from the population by employing machine learning techniques and represented as Probabilistic Graphical Models, from which new solutions can be sampled or generated from guided-crossover. 1168:

t is quite unnatural to model applications in terms of genetic operators like mutation and crossover on bit strings. The pseudobiology adds another level of complexity between you and your problem. Second, genetic algorithms take a very long time on nontrivial problems. he analogy with

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Examples of problems solved by genetic algorithms include: mirrors designed to funnel sunlight to a solar collector, antennae designed to pick up radio signals in space, walking methods for computer figures, optimal design of aerodynamic bodies in complex flowfields

518:) are typically more likely to be selected. Certain selection methods rate the fitness of each solution and preferentially select the best solutions. Other methods rate only a random sample of the population, as the former process may be very time-consuming. 1078:

It can be quite effective to combine GA with other optimization methods. A GA tends to be quite good at finding generally good global solutions, but quite inefficient at finding the last few mutations to find the absolute optimum. Other techniques (such as

1457:(EP) involves populations of solutions with primarily mutation and selection and arbitrary representations. They use self-adaptation to adjust parameters, and can include other variation operations such as combining information from multiple parents. 968:. Crossover and mutation are performed so as to respect data element boundaries. For most data types, specific variation operators can be designed. Different chromosomal data types seem to work better or worse for different specific problem domains. 1682:

modifications to the worst components. This requires that a suitable representation be selected which permits individual solution components to be assigned a quality measure ("fitness"). The governing principle behind this algorithm is that of

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Once the genetic representation and the fitness function are defined, a GA proceeds to initialize a population of solutions and then to improve it through repetitive application of the mutation, crossover, inversion and selection operators.

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are evolutionary algorithms that use human evaluation. They are usually applied to domains where it is hard to design a computational fitness function, for example, evolving images, music, artistic designs and forms to fit users' aesthetic

455:). Arrays of other types and structures can be used in essentially the same way. The main property that makes these genetic representations convenient is that their parts are easily aligned due to their fixed size, which facilitates simple 1931:

Eiben, A. E. et al (1994). "Genetic algorithms with multi-parent recombination". PPSN III: Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: 78–87.

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wrote about Evolver in 1990, and it remained the only interactive commercial genetic algorithm until 1995. Evolver was sold to Palisade in 1997, translated into several languages, and is currently in its 6th version. Since the 1990s,

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I have never encountered any problem where genetic algorithms seemed to me the right way to attack it. Further, I have never seen any computational results reported using genetic algorithms that have favorably impressed me. Stick to

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The population size depends on the nature of the problem, but typically contains hundreds or thousands of possible solutions. Often, the initial population is generated randomly, allowing the entire range of possible solutions (the

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evaluation for complex problems is often the most prohibitive and limiting segment of artificial evolutionary algorithms. Finding the optimal solution to complex high-dimensional, multimodal problems often requires very expensive

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along with a small proportion of less fit solutions. These less fit solutions ensure genetic diversity within the genetic pool of the parents and therefore ensure the genetic diversity of the subsequent generation of children.

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problems where a set of items must be split into disjoint group of items in an optimal way, would better be achieved by making characteristics of the groups of items equivalent to genes. These kind of problems include

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environment. So, one needs to reason at the population level. It is also believed BAs could be successfully applied to complex positioning problems (antennas for cell phones, urban planning, and so on) or data mining.

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For specific optimization problems and problem instances, other optimization algorithms may be more efficient than genetic algorithms in terms of speed of convergence. Alternative and complementary algorithms include

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improvement through selectively removing low-quality components and replacing them with a randomly selected component. This is decidedly at odds with a GA that selects good solutions in an attempt to make better

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originally used finite state machines for predicting environments, and used variation and selection to optimize the predictive logics. Genetic algorithms in particular became popular through the work of

488:). Occasionally, the solutions may be "seeded" in areas where optimal solutions are likely to be found or the distribution of the sampling probability tuned to focus in those areas of greater interest. 1075:. Examples are dominance & co-dominance principles and LIGA (levelized interpolative genetic algorithm), which combines a flexible GA with modified A* search to tackle search space anisotropicity. 999:

A practical variant of the general process of constructing a new population is to allow the best organism(s) from the current generation to carry over to the next, unaltered. This strategy is known as

4137: 1083:) are quite efficient at finding absolute optimum in a limited region. Alternating GA and hill climbing can improve the efficiency of GA while overcoming the lack of robustness of hill climbing. 4182:"Theory of Genetic Algorithms II: models for genetic operators over the string-tensor representation of populations and convergence to global optima for arbitrary fitness function under scaling" 624:

is employed. An adequate population size ensures sufficient genetic diversity for the problem at hand, but can lead to a waste of computational resources if set to a value larger than required.

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interactions. As such, they are aligned with the Building Block Hypothesis in adaptively reducing disruptive recombination. Prominent examples of this approach include the mGA, GEMGA and LLGA.

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Although crossover and mutation are known as the main genetic operators, it is possible to use other operators such as regrouping, colonization-extinction, or migration in genetic algorithms.

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Bies, Robert R.; Muldoon, Matthew F.; Pollock, Bruce G.; Manuck, Steven; Smith, Gwenn; Sale, Mark E. (2006). "A Genetic Algorithm-Based, Hybrid Machine Learning Approach to Model Selection".

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In the late 1980s, General Electric started selling the world's first genetic algorithm product, a mainframe-based toolkit designed for industrial processes. In 1989, Axcelis, Inc. released

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generates candidate solutions via a parameterized probability distribution. The parameters are updated via cross-entropy minimization, so as to generate better samples in the next iteration.

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is often employed. In this way, small changes in the integer can be readily affected through mutations or crossovers. This has been found to help prevent premature convergence at so-called

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can be implemented with a so-called "comma strategy" in which parents are not maintained and new parents are selected only from offspring. This can be more effective on dynamic problems.

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in nature). A recombination rate that is too high may lead to premature convergence of the genetic algorithm. A mutation rate that is too high may lead to loss of good solutions, unless

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heuristic penalizes crossover between candidate solutions that are too similar; this encourages population diversity and helps prevent premature convergence to a less optimal solution.

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like adding vectors that more probably may follow a ridge in the phenotypic landscape. Thus, the efficiency of the process may be increased by many orders of magnitude. Moreover, the

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Echegoyen, Carlos; Mendiburu, Alexander; Santana, Roberto; Lozano, Jose A. (8 November 2012). "On the Taxonomy of Optimization Problems Under Estimation of Distribution Algorithms".

1511:(GGA) is an evolution of the GA where the focus is shifted from individual items, like in classical GAs, to groups or subset of items. The idea behind this GA evolution proposed by 4324:: Learn step by step or watch global convergence in batch, change the population size, crossover rates/bounds, mutation rates/bounds and selection mechanisms, and add constraints. 819:

of the problem. This means that it does not "know how" to sacrifice short-term fitness to gain longer-term fitness. The likelihood of this occurring depends on the shape of the

947:. The notion of real-valued genetic algorithms has been offered but is really a misnomer because it does not really represent the building block theory that was proposed by 2563:

Kwon, Y.D.; Kwon, S.B.; Jin, S.B.; Kim, J.Y. (2003). "Convergence enhanced genetic algorithm with successive zooming method for solving continuous optimization problems".

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Despite the lack of consensus regarding the validity of the building-block hypothesis, it has been consistently evaluated and used as reference throughout the years. Many

421:. Commonly, the algorithm terminates when either a maximum number of generations has been produced, or a satisfactory fitness level has been reached for the population. 2035:

Harik, Georges R.; Lobo, Fernando G.; Sastry, Kumara (1 January 2006). "Linkage Learning via Probabilistic Modeling in the Extended Compact Genetic Algorithm (ECGA)".

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depends on the fitness values of the solutions. There are more examples of AGA variants: Successive zooming method is an early example of improving convergence. In

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Genetic Algorithms - Computer programs that "evolve" in ways that resemble natural selection can solve complex problems even their creators do not fully understand

907:. The suitability of genetic algorithms is dependent on the amount of knowledge of the problem; well known problems often have better, more specialized approaches. 459:

operations. Variable length representations may also be used, but crossover implementation is more complex in this case. Tree-like representations are explored in

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Numerische Optimierung von Computor-Modellen mittels der Evolutionsstrategie : mit einer vergleichenden Einführung in die Hill-Climbing- und Zufallsstrategie

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heuristic algorithms (simulated annealing, particle swarm optimization, genetic algorithm) and two direct search algorithms (simplex search, pattern search).

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proposed a "learning machine" which would parallel the principles of evolution. Computer simulation of evolution started as early as in 1954 with the work of

1626:(CA) consists of the population component almost identical to that of the genetic algorithm and, in addition, a knowledge component called the belief space. 1059:(clustering-based adaptive genetic algorithm), through the use of clustering analysis to judge the optimization states of the population, the adjustment of 3621:

Zlochin, Mark; Birattari, Mauro; Meuleau, Nicolas; Dorigo, Marco (1 October 2004). "Model-Based Search for Combinatorial Optimization: A Critical Survey".

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Patrascu, M.; Stancu, A.F.; Pop, F. (2014). "HELGA: a heterogeneous encoding lifelike genetic algorithm for population evolution modeling and simulation".

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Ting, Chuan-Kang (2005). "On the Mean Convergence Time of Multi-parent Genetic Algorithms Without Selection". Advances in Artificial Life: 403–412.

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Barricelli, Nils Aall (1963). "Numerical testing of evolution theories. Part II. Preliminary tests of performance, symbiogenesis and terrestrial life".

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spacecraft antenna. This complicated shape was found by an evolutionary computer design program to create the best radiation pattern. It is known as an

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among others, is a population-based method in which solutions are also subject to local improvement phases. The idea of memetic algorithms comes from

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in order to maintain the population diversity as well as to sustain the convergence capacity. In AGA (adaptive genetic algorithm), the adjustment of

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Pavai, G.; Geetha, T.V. (2019). "New crossover operators using dominance and co-dominance principles for faster convergence of genetic algorithms".

1609:, which unlike genes, can adapt themselves. In some problem areas they are shown to be more efficient than traditional evolutionary algorithms. 1280:. Research in GAs remained largely theoretical until the mid-1980s, when The First International Conference on Genetic Algorithms was held in 1141:

algorithm might get stuck in. Observe that commonly used crossover operators cannot change any uniform population. Mutation alone can provide

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obtained from using real-valued chromosomes. This was explained as the set of real values in a finite population of chromosomes as forming a

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and possibly randomly mutated) to form a new generation. The new generation of candidate solutions is then used in the next iteration of the

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Sadowski, Krzysztof L.; Bosman, Peter A.N.; Thierens, Dirk (1 January 2013). "On the usefulness of linkage processing for solving MAX-SAT".

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A description of a heuristic that performs adaptation by identifying and recombining "building blocks", i.e. low order, low defining-length

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The highest ranking solution's fitness is reaching or has reached a plateau such that successive iterations no longer produce better results

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Numerical optimization of computer models (Translation of 1977 Numerische Optimierung von Computor-Modellen mittels der Evolutionsstrategie

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probability and population size to find reasonable settings for the problem class being worked on. A very small mutation rate may lead to

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self-tuning of critical parameters. Methodologies of interest for Reactive Search include machine learning and statistics, in particular

4275: 2862:"Aerodynamic optimisation of a hypersonic reentry vehicle based on solution of the Boltzmann–BGK equation and evolutionary optimisation" 3747:

Civicioglu, P. (2012). "Transforming Geocentric Cartesian Coordinates to Geodetic Coordinates by Using Differential Search Algorithm".

3333:"Benchmarks for Evaluating Optimization Algorithms and Benchmarking MATLAB Derivative-Free Optimizers for Practitioners' Rapid Access" 3247: 2712: 979:, in which too many simultaneous mutations (or crossover events) must occur in order to change the chromosome to a better solution. 4641: 3722: 824: 4327: 808:

The "better" solution is only in comparison to other solutions. As a result, the stopping criterion is not clear in every problem.

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Zhang, J.; Chung, H.; Lo, W. L. (2007). "Clustering-Based Adaptive Crossover and Mutation Probabilities for Genetic Algorithms".

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A variation, where the population as a whole is evolved rather than its individual members, is known as gene pool recombination.

85: 4378: 1737: 1322: 221: 3400: 1570:) uses many ants (or agents) equipped with a pheromone model to traverse the solution space and find locally productive areas. 4123: 3605: 3526: 3493: 3394: 3230: 3205: 3165: 3105: 3069: 3044: 2917: 2307: 2132: 2052: 1954: 1819: 255: 4298: 3674: 4636: 4428: 4423: 4418: 1574: 1460: 710: 550: 90: 4351: 3299: 2546: 2280:

Wolpert, D.H., Macready, W.G., 1995. No Free Lunch Theorems for Optimisation. Santa Fe Institute, SFI-TR-05-010, Santa Fe.

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has the opportunity to place steps in consecutive order or any other suitable order in favour of survival or efficiency.

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The practical use of a genetic algorithm has limitations, especially as compared to alternative optimization algorithms:

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This generational process is repeated until a termination condition has been reached. Common terminating conditions are:

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Shir, Ofer M. (2012). "Niching in Evolutionary Algorithms". In Rozenberg, Grzegorz; Bäck, Thomas; Kok, Joost N. (eds.).

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The next step is to generate a second generation population of solutions from those selected, through a combination of

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Schwefel, Hans-Paul (1974): Numerische Optimierung von Computer-Modellen (PhD thesis). Reprinted by Birkhäuser (1977).

382:) to an optimization problem is evolved toward better solutions. Each candidate solution has a set of properties (its 4371: 4104: 3448: 2224: 2079: 2007: 1937: 1417: 766: 168: 688:

A hypothesis that a genetic algorithm performs adaptation by implicitly and efficiently implementing this heuristic.

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of the solution is the sum of values of all objects in the knapsack if the representation is valid, or 0 otherwise.

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Cha, Sung-Hyuk; Tappert, Charles C. (2009). "A Genetic Algorithm for Constructing Compact Binary Decision Trees".

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02.27.96 - UC Berkeley's Hans Bremermann, professor emeritus and pioneer in mathematical biology, has died at 69

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Although Barricelli, in work he reported in 1963, had simulated the evolution of ability to play a simple game,

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Coffin, David; Smith, Robert E. (1 January 2008). "Linkage Learning in Estimation of Distribution Algorithms".

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in the 1960s and early 1970s – Rechenberg's group was able to solve complex engineering problems through

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and guarantees that the solution quality obtained by the GA will not decrease from one generation to the next.

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GAs cannot effectively solve problems in which the only fitness measure is a binary pass/fail outcome (like

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in which computer programs, rather than function parameters, are optimized. Genetic programming often uses

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An Approach to Designing an Unmanned Helicopter Autopilot Using Genetic Algorithms and Simulated Annealing

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Pareto Optimal Reconfiguration of Power Distribution Systems Using a Genetic Algorithm Based on NSGA-II.

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every such representation is valid, as the size of objects may exceed the capacity of the knapsack. The

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may be one of the most promising approaches to convincingly use GA to solve complex real life problems.

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is used to determine the air resistance of a vehicle whose shape is encoded as the phenotype), or even

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Hierarchical Bayesian optimization algorithm : toward a new generation of evolutionary algorithms

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A Genetic Algorithm Tutorial by Darrell Whitley Computer Science Department Colorado State University

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Luque-Rodriguez, Maria; Molina-Baena, Jose; Jimenez-Vilchez, Alfonso; Arauzo-Azofra, Antonio (2022).

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hybridized with the Dominance Criterion of Martello and Toth, is arguably the best technique to date.

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As a general rule of thumb genetic algorithms might be useful in problem domains that have a complex

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structures typical of genetic algorithms. There are many variants of Genetic Programming, including

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neighboring individuals for selection and reproduction. Other variants, like genetic algorithms for

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In some problems, it is hard or even impossible to define the fitness expression; in these cases, a

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of the represented solution. The fitness function is always problem-dependent. For instance, in the

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Taherdangkoo, Mohammad; Paziresh, Mahsa; Yazdi, Mehran; Bagheri, Mohammad Hadi (19 November 2012).

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Opinion is divided over the importance of crossover versus mutation. There are many references in

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An online interactive Genetic Algorithm tutorial for a reader to practise or learn how a GA works

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An excellent introduction to GA by John Holland and with an application to the Prisoner's Dilemma

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depends on these optimization states. Recent approaches use more abstract variables for deciding

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Problems which appear to be particularly appropriate for solution by genetic algorithms include

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Learning linkage to efficiently solve problems of bounded difficulty using genetic algorithms

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of every individual in the population is evaluated; the fitness is usually the value of the

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The evolution usually starts from a population of randomly generated individuals, and is an

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Li, Lin; Saldivar, Alfredo Alan Flores; Bai, Yun; Chen, Yi; Liu, Qunfeng; Li, Yun (2019).

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evolution—where significant progress require millions of years—can be quite appropriate.

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Proceedings of the 2018 International Conference on Computing and Artificial Intelligence

1752: 1732: 1663: 1639: 1635: 1525: 1466: 1176: 1115: 1016: 884: 880: 829: 538: 460: 298: 192: 125: 80: 3760: 3572: 3348: 2677: 1110:, and many scheduling software packages are based on GAs. GAs have also been applied to 4517: 4486: 4456: 4267: 3869: 3799: 3714: 3656: 3558: 3458: 3411: 3362: 3138: 2691: 2642: 2607: 2504: 2469: 2397: 2317: 2230: 2181: 2089: 1972: 1898: 1866: 1825: 1623: 1550: 1444: 1297: 1293: 1269: 1257: 1244: 1240: 1039:, AGAs utilize the population information in each generation and adaptively adjust the 1011: 948: 940: 892: 872: 853: 833: 406: 375: 282: 75: 55: 4198: 4181: 4171: 4154: 2576: 1806:. ICCAI 2018. New York, NY, USA: Association for Computing Machinery. pp. 19–22. 4656: 4631: 4621: 4575: 4560: 4219: 4119: 4100: 4081: 4062: 4043: 4036: 4020: 4001: 3982: 3963: 3917: 3861: 3830: 3823: 3803: 3648: 3601: 3522: 3489: 3444: 3390: 3366: 3253: 3226: 3201: 3161: 3101: 3065: 3040: 2913: 2883: 2695: 2646: 2473: 2303: 2220: 2173: 2165: 2128: 2075: 2048: 2003: 1950: 1933: 1870: 1815: 1598: 1265: 1248: 1122: 957: 820: 511: 402: 314: 100: 65: 3873: 3381: 3142: 2508: 2401: 2185: 1829: 4666: 4646: 4616: 4606: 4271: 4259: 4193: 4166: 3944: 3900: 3853: 3791: 3764: 3718: 3706: 3685: 3640: 3514: 3352: 3130: 3012: 2942: 2873: 2681: 2634: 2611: 2599: 2572: 2538: 2496: 2459: 2389: 2295: 2261: 2234: 2212: 2204: 2157: 2120: 2040: 1995: 1976: 1912: 1856: 1807: 1529: 1482: 864: 789: 784: 572: 526: 515: 436: 354: 326: 294: 236: 130: 4059:

Genetic Programming: On the Programming of Computers by Means of Natural Selection

2738: 832:) because crossing over a homogeneous population does not yield new solutions. In 4601: 4346: 4212: 3660: 3518: 3306: 2716: 2250:"An efficient algorithm for function optimization: modified stem cells algorithm" 2201:

Proceedings of the 15th annual conference on Genetic and evolutionary computation

1999: 1742: 1236: 521:

The fitness function is defined over the genetic representation and measures the

485: 321:(EA). Genetic algorithms are commonly used to generate high-quality solutions to 286: 45: 3768: 3357: 3332: 2686: 2661: 2523: 2124: 2044: 1845:"Neuroevolutionary representations for learning heterogeneous treatment effects" 413:

selected from the current population, and each individual's genome is modified (

4565: 4527: 4496: 4333: 3933:"Simulation of Genetic Systems by Automatic Digital Computers. I. Introduction" 3001:"Simulation of genetic systems by automatic digital computers. I. Introduction" 2946: 2861: 2772: 1861: 1844: 1757: 1719: 1478: 1235:

only became a widely recognized optimization method as a result of the work of

1225: 965: 936: 816: 595: 506:

to reproduce for a new generation. Individual solutions are selected through a

110: 4132:

Rechenberg, Ingo (1994): Evolutionsstrategie '94, Stuttgart: Fromman-Holzboog.

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Kjellström, G. (December 1991). "On the Efficiency of Gaussian Adaptation".

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The Design of Innovation: Lessons from and for Competent Genetic Algorithms

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Sharapov, R.R.; Lapshin, A.V. (2006). "Convergence of genetic algorithms".

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During each successive generation, a portion of the existing population is

3821:

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3509:

Thierens, Dirk (11 September 2010). "The Linkage Tree Genetic Algorithm".

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Evolutionary Computation: Toward a New Philosophy of Machine Intelligence

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Proceedings of the Fourth International Conference on Genetic Algorithms

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Pelikan, Martin; Goldberg, David E.; Cantú-Paz, Erick (1 January 1999).

1901:"Initialization of Feature Selection Search for Classification (sec. 3)" 1224:

George Friedman, and Michael Conrad. Many early papers are reprinted by

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Deb, Kalyanmoy; Spears, William M. (1997). "C6.2: Speciation methods".

1800:"Genetic Algorithms with Local Optima Handling to Solve Sudoku Puzzles" 1402: in this section. Unsourced material may be challenged and removed. 1142: 840:, diversity is not essential because of a greater reliance on mutation. 797:

that is computationally efficient. It is apparent that amalgamation of

751: in this section. Unsourced material may be challenged and removed. 617: 409:

in the optimization problem being solved. The more fit individuals are

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Schmitt, Lothar M.; Nehaniv, Chrystopher L.; Fujii, Robert H. (1998).

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problems, introduce time-dependence or noise in the fitness function.

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A comparison of particle swarm optimization and the genetic algorithm

3673:

Rania Hassan, Babak Cohanim, Olivier de Weck, Gerhard Vente r (2005)

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can obtain. Researchers have analyzed GA convergence analytically.

636:

may be employed to make the calculation faster or more robust. The

387: 2777:(PhD). Dept. Computer Science, University of Michigan, Ann Arbour. 4354:

Tutorial with the intuition behind GAs and Python implementation.

4304:

An Overview of the History and Flavors of Evolutionary Algorithms

1743:

Genetic algorithms in signal processing (a.k.a. particle filters)

1481:

to represent the computer programs for adaptation instead of the

1296:, the world's first commercial GA product for desktop computers. 1247:. Another approach was the evolutionary programming technique of 939:

representations. The floating point representation is natural to

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Evolutionary algorithms for the physical design of VLSI circuits

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Gerges, Firas; Zouein, Germain; Azar, Danielle (12 March 2018).

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probability of mutation when the solution quality drops (called

4451: 4321: 4015:

Hingston, Philip; Barone, Luigi; Michalewicz, Zbigniew (2008).

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Genetic Algorithms in Search, Optimization and Machine Learning

2147: 1448: 1306: 1114:. Genetic algorithms are often applied as an approach to solve 2290:

Goldberg, David E. (1991). "The theory of virtual alphabets".

1219:

essential elements of modern genetic algorithms. In addition,

443:

A standard representation of each candidate solution is as an

4340:

Global Optimization Algorithms – Theory and Application

3692:"Automatic Test Case Optimization: A Bacteriologic Algorithm" 3620: 2760:

Gene expression: The missing link in evolutionary computation

1251:, which was proposed for generating artificial intelligence. 598:(2006) that support the importance of mutation-based search. 4300:

Provides a list of resources in the genetic algorithms field

467:; a mix of both linear chromosomes and trees is explored in 4358:

Genetic Algorithms evolves to solve the prisoner's dilemma.

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In many problems, GAs have a tendency to converge towards

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Genetic Algorithms + Data Structures = Evolution Programs

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Numerische Optimierung von Computer-Modellen (PhD thesis)

2418:

Removing the genetics from the standard genetic algorithm

541:

may be used to determine the fitness function value of a

4452:

Covariance Matrix Adaptation Evolution Strategy (CMA-ES)

3843: 3436: 2847:"Flexible Muscle-Based Locomotion for Bipedal Creatures" 2067: 1692: 696:"Short, low order, and highly fit schemata are sampled, 2971: 2926: 1882: 1880: 927:

The simplest algorithm represents each chromosome as a

3275:"What's the Best Answer? It's Survival of the Fittest" 2198: 2016: 971:

When bit-string representations of integers are used,

931:. Typically, numeric parameters can be represented by 341:. Some examples of GA applications include optimizing 329:

by relying on biologically inspired operators such as

2963:(1954). "Esempi numerici di processi di evoluzione". 2953: 2830:

Automated Antenna Design with Evolutionary Algorithms

4214:

The Simple Genetic Algorithm: Foundations and Theory

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Design by Evolution: Advances in Evolutionary Design

3319:

Evolver: Sophisticated Optimization for Spreadsheets

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of the overall genetic algorithm process (seen as a

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A solution is found that satisfies minimum criteria

16:

Competitive algorithm for searching a problem space

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(2008). 3846:Journal of Pharmacokinetics and Pharmacodynamics 2037:Scalable Optimization via Probabilistic Modeling 1797: 671: 3784:Journal of Optimization Theory and Applications 2736: 2659: 2521: 2486: 2034: 1590:Evolutionary computation is a sub-field of the 1133:, is designed to move the population away from 632:In addition to the main operators above, other 463:and graph-form representations are explored in 2562: 1650: 1515:is that solving some complex problems, a.k.a. 1260:in the early 1970s, and particularly his book 1214:published a series of papers on simulation of 378:(called individuals, creatures, organisms, or 4379: 3511:Parallel Problem Solving from Nature, PPSN XI 3031: 2592:IEEE Transactions on Evolutionary Computation 2414: 1642:) of the Gaussian simultaneously keeping the 1632:(DE) inspired by migration of superorganisms. 692:Goldberg describes the heuristic as follows: 256: 4393: 4336:, 2009 (225 p). Free open text by Sean Luke. 4038:Adaptation in Natural and Artificial Systems 4000:(3rd ed.). Piscataway, NJ: IEEE Press. 3962:. Reading, MA: Addison-Wesley Professional. 2826: 2660:Li, J.C.F.; Zimmerle, D.; Young, P. (2022). 2373: 2283: 1843:Burkhart, Michael C.; Ruiz, Gabriel (2023). 1842: 1264:(1975). His work originated with studies of 1262:Adaptation in Natural and Artificial Systems 916: 4075: 3981:. Norwell, MA: Kluwer Academic Publishers. 3825:Genetic Programming – An Introduction 3098:Evolutionary Computation: The Fossil Record 3053: 3025: 2859: 2827:Hornby, G. S.; Linden, D. S.; Lohn, J. D., 2803:"A solar energy system that tracks the sun" 2737:Goldberg, D. E.; Korb, B.; Deb, K. (1989). 2624: 2336: 2114: 1905:Journal of Artificial Intelligence Research 1469:(GP) is a related technique popularized by 1006: 4386: 4372: 3911: 3880: 3781: 3746: 3592: 3586: 3309:. Futuresmag.com. Retrieved on 2013-08-07. 3155: 3120: 2977: 2959: 1655:Metaheuristic methods broadly fall within 1436:Evolutionary algorithms is a sub-field of 1367: 604:It is worth tuning parameters such as the 263: 249: 4253: 4197: 4170: 3948: 3937:Australian Journal of Biological Sciences 3894: 3634: 3562: 3356: 3245: 3016: 2877: 2860:Evans, B.; Walton, S.P. (December 2017). 2685: 2463: 2265: 1966: 1916: 1860: 1418:Learn how and when to remove this message 815:or even arbitrary points rather than the 767:Learn how and when to remove this message 4642:No free lunch in search and optimization 4094: 3976: 3957: 3598:Genetic Algorithms and Grouping Problems 3541: 3508: 3440:BOA: The Bayesian Optimization Algorithm 3220: 3195: 3180: 2343:Janikow, C. Z.; Michalewicz, Z. (1991). 2289: 2071:BOA: The Bayesian Optimization Algorithm 2022: 1785: 365: 276: 4232: 4179: 4152: 4138:"Linear analysis of genetic algorithms" 4033: 3883:Journal of Pattern Recognition Research 3483: 3272: 2445: 2415:Baluja, Shumeet; Caruana, Rich (1995). 2254:Central European Journal of Engineering 1886: 1586:Other evolutionary computing algorithms 1179:for your heuristic search voodoo needs. 4688: 4330:An excellent tutorial with much theory 3930: 3914:Introduction to Evolutionary Computing 3829:. San Francisco, CA: Morgan Kaufmann. 3379: 3059: 2995: 2932: 2900: 1738:List of genetic algorithm applications 1323:List of genetic algorithm applications 1287: 424:A typical genetic algorithm requires: 4367: 4097:An Introduction to Genetic Algorithms 3995: 3912:Eiben, Agoston; Smith, James (2003). 3298:Ruggiero, Murray A.. (1 August 2009) 3095: 2800: 2770: 1693:Other stochastic optimisation methods 1555:Swarm intelligence is a sub-field of 1544: 1316: 711:estimation of distribution algorithms 4637:Interactive evolutionary computation 4429:Interactive evolutionary computation 4424:Human-based evolutionary computation 4419:Evolutionary multimodal optimization 4281:from the original on 9 October 2022. 4209: 4116:A Field Guide to Genetic Programming 4056: 3690:; Yves Le Traon (March–April 2005). 3582:from the original on 9 October 2022. 3406:from the original on 9 October 2022. 3321:. Palisade. Retrieved on 2013-08-07. 2552:from the original on 9 October 2022. 2435:from the original on 9 October 2022. 2292:Parallel Problem Solving from Nature 1989: 1969:Handbook of Evolutionary Computation 1575:Estimation of distribution algorithm 1461:Estimation of Distribution Algorithm 1400:adding citations to reliable sources 1371: 1332:Genetic algorithms are a sub-field: 1202:, who was using the computer at the 749:adding citations to reliable sources 720: 556: 317:that belongs to the larger class of 91:Evolutionary multimodal optimization 3728:from the original on 9 October 2022 3488:(1st ed.). Berlin : Springer. 2361:from the original on 9 October 2022 2117:Linkage in Evolutionary Computation 1971:. Institute of Physics Publishing. 1537:Interactive evolutionary algorithms 1108:timetabling and scheduling problems 655:Fixed number of generations reached 13: 4675:Evolutionary Computation (journal) 3645:10.1023/B:ANOR.0000039526.52305.af 2522:Srinivas, M.; Patnaik, L. (1994). 1101: 14: 4727: 4287: 3064:. London: John Wiley & Sons. 1362: 1031:Instead of using fixed values of 579:(also called recombination), and 478: 3686:Baudry, Benoit; Franck Fleurey; 3273:Markoff, John (29 August 1990). 3200:. Basel; Stuttgart: Birkhäuser. 1849:Journal of Computational Science 1376: 1327: 725: 439:to evaluate the solution domain. 345:for better performance, solving 116:Promoter based genetic algorithm 4447:Cellular evolutionary algorithm 3813: 3775: 3740: 3679: 3667: 3614: 3535: 3502: 3477: 3430: 3389:. Springer, pp. 683-712, 2003. 3380:Cohoon, J; et al. (2002). 3373: 3324: 3312: 3292: 3266: 3239: 3225:. Chichester; New York: Wiley. 3214: 3189: 3174: 3160:. 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(2001). 3769:10.1016/j.cageo.2011.12.011 3358:10.1109/ACCESS.2019.2923092 3037:Computer Models in Genetics 2906:The Algorithm Design Manual 2725:edge recombination operator 2721:travelling salesman problem 2687:10.1016/j.egyai.2022.100186 2125:10.1007/978-3-540-85068-7_7 2045:10.1007/978-3-540-34954-9_3 1768:Rule-based machine learning 1726: 1651:Other metaheuristic methods 1579:Particle swarm optimization 1491:Gene expression programming 911: 901:particle swarm optimization 685:with above average fitness. 469:gene expression programming 351:hyperparameter optimization 313:inspired by the process of 96:Particle swarm optimization 10: 4732: 4502:Learning classifier system 4492:Natural evolution strategy 4360:Written by Robert Axelrod. 4095:Mitchell, Melanie (1996). 3749:Computers &Geosciences 3300:Fifteen years and counting 3035:; Burnell, Donald (1970). 2565:Computers & Structures 1862:10.1016/j.jocs.2023.102054 1763:Learning classifier system 1548: 1509:Grouping genetic algorithm 1499:Linear genetic programming 1429: 1320: 1189: 994: 964:, or any other imaginable 920: 905:integer linear programming 560: 495: 401:. In each generation, the 370:In a genetic algorithm, a 207:Linear genetic programming 154:Clonal selection algorithm 106:Natural evolution strategy 4665: 4584: 4551: 4510: 4437: 4401: 4345:11 September 2008 at the 4233:Whitley, Darrell (1994). 3858:10.1007/s10928-006-9004-6 3262:– via Google Books. 3246:Aldawoodi, Namir (2008). 3156:Rechenberg, Ingo (1973). 3039:. New York: McGraw-Hill. 2879:10.1016/j.apm.2017.07.024 2639:10.1007/s00500-018-3016-1 2501:10.1134/S1054661806030084 2465:10.1007/s00440-003-0330-y 2394:10.1007/s00500-014-1401-y 2267:10.2478/s13531-012-0047-8 1700:cross-entropy (CE) method 917:Chromosome representation 667:Combinations of the above 4467:Evolutionary programming 4414:Evolutionary data mining 4395:Evolutionary computation 4242:Statistics and Computing 3977:Goldberg, David (2002). 3958:Goldberg, David (1989). 3931:Fraser, Alex S. (1957). 3484:Pelikan, Martin (2005). 3100:. New York: IEEE Press. 3060:Crosby, Jack L. 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Springer-Verlag. 4057:Koza, John (1992). 3761:2012CG.....46..229C 3594:Falkenauer, Emanuel 3573:2001cs........2027F 3349:2019IEEEA...779657L 3158:Evolutionsstrategie 2678:2022EneAI..1000186L 1753:Universal Darwinism 1733:Genetic programming 1664:Simulated annealing 1640:average information 1636:Gaussian adaptation 1601:(MA), often called 1467:Genetic programming 1309:has built in three 1288:Commercial products 1177:simulated annealing 1137:that a traditional 1116:global optimization 1017:online optimization 885:Gaussian adaptation 881:simulated annealing 830:genetic programming 461:genetic programming 376:candidate solutions 299:operations research 222:Genetic Improvement 193:Genetic programming 126:Self-modifying code 81:Gaussian adaptation 4696:Genetic algorithms 4518:Swarm intelligence 4511:Related techniques 4487:Evolution strategy 4457:Cultural algorithm 4264:10.1007/BF00175354 3796:10.1007/BF00941405 3711:10.1109/MS.2005.30 3688:Jean-Marc Jézéquel 3135:10.1007/BF01556602 3123:Acta Biotheoretica 3005:Aust. J. Biol. Sci 2771:Harik, G. (1997). 2300:10.1007/BFb0029726 1624:Cultural algorithm 1551:Swarm intelligence 1545:Swarm intelligence 1528:, line balancing, 1513:Emanuel Falkenauer 1317:Related techniques 1300:technology writer 1298:The New York Times 1241:Hans-Paul Schwefel 1089:inversion operator 949:John Henry Holland 893:swarm intelligence 799:approximate models 407:objective function 291: 76:Evolution strategy 56:Cultural algorithm 4716:Digital organisms 4706:Search algorithms 4683: 4682: 4657:Program synthesis 4632:Genetic operators 4622:Fitness landscape 4576:Memetic algorithm 4561:Firefly algorithm 4472:Genetic algorithm 4125:978-1-4092-0073-4 3950:10.1071/BI9570484 3607:978-0-471-97150-4 3528:978-3-642-15843-8 3495:978-3-540-23774-7 3396:978-3-540-43330-9 3232:978-0-471-09988-8 3207:978-3-7643-0876-6 3167:978-3-7728-0373-4 3107:978-0-7803-3481-6 3071:978-0-471-18880-3 3046:978-0-07-021904-5 3018:10.1071/BI9570484 2919:978-1-849-96720-4 2708:See for instance 2633:(11): 3661–3686. 2571:(17): 1715–1725. 2543:10.1109/21.286385 2388:(12): 2565–2576. 2309:978-3-540-54148-6 2134:978-3-540-85067-0 2054:978-3-540-34953-2 1955:978-3-540-28848-0 1821:978-1-4503-6419-5 1615:(BA) inspired by 1599:Memetic algorithm 1428: 1427: 1420: 1266:cellular automata 1249:Lawrence J. Fogel 1125:as mixing, i.e., 1123:fitness landscape 1001:elitist selection 865:decision problems 850:random immigrants 821:fitness landscape 777: 776: 769: 664:Manual inspection 622:elitist selection 573:genetic operators 557:Genetic operators 395:iterative process 315:natural selection 303:genetic algorithm 273: 272: 140:Genetic algorithm 101:Memetic algorithm 86:Grammar induction 66:Effective fitness 4723: 4647:Machine learning 4617:Fitness function 4607:Digital organism 4388: 4381: 4374: 4365: 4364: 4282: 4280: 4257: 4239: 4229: 4217: 4203: 4201: 4192:(1–3): 181–231. 4176: 4174: 4149: 4129: 4110: 4091: 4072: 4053: 4041: 4030: 4011: 3992: 3973: 3954: 3952: 3927: 3908: 3898: 3877: 3840: 3828: 3808: 3807: 3779: 3773: 3772: 3744: 3738: 3737: 3735: 3733: 3727: 3696: 3683: 3677: 3671: 3665: 3664: 3638: 3629:(1–4): 373–395. 3618: 3612: 3611: 3590: 3584: 3583: 3581: 3566: 3548: 3539: 3533: 3532: 3506: 3500: 3499: 3481: 3475: 3474: 3468: 3464: 3462: 3454: 3434: 3428: 3427: 3421: 3417: 3415: 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2058: 2032: 2026: 2020: 2014: 2013: 1987: 1981: 1980: 1964: 1958: 1947: 1941: 1929: 1923: 1922: 1920: 1896: 1890: 1884: 1875: 1874: 1864: 1840: 1834: 1833: 1795: 1789: 1783: 1423: 1416: 1412: 1409: 1403: 1380: 1372: 1185: 985:virtual alphabet 790:fitness function 785:fitness function 772: 765: 761: 758: 752: 729: 721: 527:knapsack problem 516:fitness function 437:fitness function 355:causal inference 295:computer science 265: 258: 251: 237:Parity benchmark 131:Polymorphic code 19: 18: 4731: 4730: 4726: 4725: 4724: 4722: 4721: 4720: 4686: 4685: 4684: 4679: 4661: 4602:Artificial life 4580: 4547: 4506: 4433: 4397: 4392: 4347:Wayback Machine 4312: 4295: 4290: 4285: 4278: 4255:10.1.1.184.3999 4237: 4226: 4126: 4107: 4088: 4069: 4050: 4027: 4008: 3989: 3970: 3924: 3896:10.1.1.154.8314 3837: 3816: 3811: 3780: 3776: 3745: 3741: 3731: 3729: 3725: 3694: 3684: 3680: 3672: 3668: 3619: 3615: 3608: 3591: 3587: 3579: 3551:Complex Systems 3546: 3540: 3536: 3529: 3507: 3503: 3496: 3482: 3478: 3466: 3465: 3456: 3455: 3451: 3435: 3431: 3419: 3418: 3409: 3408: 3403: 3397: 3386: 3378: 3374: 3343:: 79657–79670. 3329: 3325: 3317: 3313: 3307:Wayback Machine 3297: 3293: 3283: 3281: 3271: 3267: 3260: 3244: 3240: 3233: 3219: 3215: 3208: 3194: 3190: 3179: 3175: 3168: 3154: 3150: 3129:(3–4): 99–126. 3119: 3115: 3108: 3094: 3090: 3083: 3079: 3072: 3058: 3054: 3047: 3030: 3026: 2994: 2990: 2976: 2972: 2958: 2954: 2931: 2927: 2920: 2899: 2895: 2858: 2854: 2845: 2844: 2840: 2833: 2825: 2821: 2811: 2809: 2799: 2795: 2786: 2782: 2769: 2765: 2758: 2754: 2743:Complex Systems 2735: 2731: 2717:Wayback Machine 2707: 2703: 2666:Energy & AI 2658: 2654: 2623: 2619: 2588: 2584: 2561: 2557: 2549: 2526: 2520: 2516: 2485: 2481: 2444: 2440: 2432: 2421: 2413: 2409: 2378: 2374: 2364: 2362: 2358: 2347: 2341: 2337: 2325: 2324: 2315: 2314: 2310: 2288: 2284: 2279: 2275: 2246: 2242: 2227: 2197: 2193: 2146: 2142: 2135: 2113: 2109: 2097: 2096: 2087: 2086: 2082: 2066: 2062: 2055: 2033: 2029: 2021: 2017: 2010: 1988: 1984: 1965: 1961: 1948: 1944: 1930: 1926: 1897: 1893: 1885: 1878: 1841: 1837: 1822: 1796: 1792: 1784: 1780: 1776: 1729: 1716:neural networks 1695: 1653: 1588: 1553: 1547: 1479:data structures 1434: 1424: 1413: 1407: 1404: 1393: 1381: 1370: 1365: 1330: 1325: 1319: 1290: 1268:, conducted by 1237:Ingo Rechenberg 1192: 1186: 1183: 1104: 1102:Problem domains 1025: 1009: 997: 925: 919: 914: 773: 762: 756: 753: 742: 730: 719: 674: 646: 630: 569: 561:Main articles: 559: 510:process, where 500: 494: 481: 368: 363: 327:search problems 287:evolved antenna 269: 46:Artificial life 17: 12: 11: 5: 4729: 4719: 4718: 4713: 4708: 4703: 4698: 4681: 4680: 4678: 4677: 4671: 4669: 4663: 4662: 4660: 4659: 4654: 4649: 4644: 4639: 4634: 4629: 4624: 4619: 4614: 4609: 4604: 4599: 4594: 4588: 4586: 4585:Related topics 4582: 4581: 4579: 4578: 4573: 4568: 4566:Harmony search 4563: 4557: 4555: 4549: 4548: 4546: 4545: 4540: 4535: 4530: 4528:Bees algorithm 4525: 4520: 4514: 4512: 4508: 4507: 4505: 4504: 4499: 4497:Neuroevolution 4494: 4489: 4484: 4479: 4474: 4469: 4464: 4459: 4454: 4449: 4443: 4441: 4435: 4434: 4432: 4431: 4426: 4421: 4416: 4411: 4405: 4403: 4399: 4398: 4391: 4390: 4383: 4376: 4368: 4362: 4361: 4355: 4349: 4337: 4331: 4325: 4319: 4311: 4308: 4307: 4306: 4301: 4294: 4291: 4289: 4288:External links 4286: 4284: 4283: 4230: 4225:978-0262220583 4224: 4207: 4204: 4177: 4150: 4133: 4130: 4124: 4111: 4105: 4092: 4087:978-3540606765 4086: 4073: 4068:978-0262111706 4067: 4054: 4049:978-0262581110 4048: 4031: 4026:978-3540741091 4025: 4012: 4007:978-0471669517 4006: 3993: 3988:978-1402070983 3987: 3974: 3969:978-0201157673 3968: 3955: 3943:(4): 484–491. 3928: 3923:978-3540401841 3922: 3909: 3905:10.13176/11.44 3878: 3852:(2): 196–221. 3841: 3836:978-1558605107 3835: 3817: 3815: 3812: 3810: 3809: 3790:(3): 589–597. 3774: 3739: 3678: 3666: 3613: 3606: 3585: 3534: 3527: 3501: 3494: 3476: 3467:|journal= 3449: 3429: 3420:|journal= 3395: 3372: 3323: 3311: 3291: 3279:New York Times 3265: 3259:978-0549773498 3258: 3252:. p. 99. 3238: 3231: 3213: 3206: 3188: 3173: 3166: 3148: 3113: 3106: 3088: 3077: 3070: 3052: 3045: 3024: 3011:(4): 484–491. 2988: 2970: 2952: 2925: 2918: 2902:Skiena, Steven 2893: 2852: 2838: 2819: 2793: 2780: 2763: 2752: 2729: 2719:or example in 2701: 2652: 2617: 2598:(3): 326–335. 2582: 2555: 2537:(4): 656–667. 2514: 2495:(3): 392–397. 2479: 2438: 2407: 2382:Soft Computing 2372: 2335: 2326:|journal= 2308: 2282: 2273: 2240: 2225: 2191: 2156:(3): 471–495. 2140: 2133: 2107: 2098:|journal= 2080: 2060: 2053: 2027: 2015: 2008: 1982: 1959: 1942: 1924: 1891: 1876: 1835: 1820: 1790: 1777: 1775: 1772: 1771: 1770: 1765: 1760: 1758:Metaheuristics 1755: 1750: 1745: 1740: 1735: 1728: 1725: 1724: 1723: 1720:metaheuristics 1703: 1694: 1691: 1690: 1689: 1673: 1667: 1652: 1649: 1648: 1647: 1633: 1627: 1621: 1610: 1587: 1584: 1583: 1582: 1571: 1549:Main article: 1546: 1543: 1542: 1541: 1534: 1506: 1464: 1458: 1452: 1430:Main article: 1426: 1425: 1384: 1382: 1375: 1369: 1366: 1364: 1363:Related fields 1361: 1360: 1359: 1354: 1349: 1347:Metaheuristics 1344: 1339: 1329: 1326: 1318: 1315: 1289: 1286: 1191: 1188: 1181: 1103: 1100: 1024: 1021: 1008: 1005: 996: 993: 966:data structure 937:floating point 921:Main article: 918: 915: 913: 910: 909: 908: 868: 861: 841: 817:global optimum 809: 806: 802: 775: 774: 733: 731: 724: 718: 715: 707: 706: 702: 701: 690: 689: 686: 673: 670: 669: 668: 665: 662: 659: 656: 653: 645: 642: 629: 626: 616:(which is non- 558: 555: 496:Main article: 493: 490: 480: 479:Initialization 477: 441: 440: 433: 411:stochastically 367: 364: 362: 359: 347:sudoku puzzles 343:decision trees 281:The 2006 NASA 271: 270: 268: 267: 260: 253: 245: 242: 241: 240: 239: 234: 229: 224: 219: 214: 209: 204: 196: 195: 189: 188: 187: 186: 181: 176: 171: 169:Genetic memory 166: 161: 156: 151: 143: 142: 136: 135: 134: 133: 128: 123: 118: 113: 111:Neuroevolution 108: 103: 98: 93: 88: 83: 78: 73: 68: 63: 58: 53: 48: 43: 35: 34: 28: 27: 15: 9: 6: 4: 3: 2: 4728: 4717: 4714: 4712: 4709: 4707: 4704: 4702: 4699: 4697: 4694: 4693: 4691: 4676: 4673: 4672: 4670: 4668: 4664: 4658: 4655: 4653: 4650: 4648: 4645: 4643: 4640: 4638: 4635: 4633: 4630: 4628: 4625: 4623: 4620: 4618: 4615: 4613: 4610: 4608: 4605: 4603: 4600: 4598: 4595: 4593: 4590: 4589: 4587: 4583: 4577: 4574: 4572: 4569: 4567: 4564: 4562: 4559: 4558: 4556: 4554: 4550: 4544: 4541: 4539: 4536: 4534: 4533:Cuckoo search 4531: 4529: 4526: 4524: 4521: 4519: 4516: 4515: 4513: 4509: 4503: 4500: 4498: 4495: 4493: 4490: 4488: 4485: 4483: 4480: 4478: 4475: 4473: 4470: 4468: 4465: 4463: 4460: 4458: 4455: 4453: 4450: 4448: 4445: 4444: 4442: 4440: 4436: 4430: 4427: 4425: 4422: 4420: 4417: 4415: 4412: 4410: 4407: 4406: 4404: 4400: 4396: 4389: 4384: 4382: 4377: 4375: 4370: 4369: 4366: 4359: 4356: 4353: 4350: 4348: 4344: 4341: 4338: 4335: 4332: 4329: 4326: 4323: 4320: 4317: 4314: 4313: 4305: 4302: 4299: 4297: 4296: 4277: 4273: 4269: 4265: 4261: 4256: 4251: 4247: 4243: 4236: 4231: 4227: 4221: 4216: 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3450:9781558606111 3446: 3442: 3441: 3433: 3425: 3413: 3402: 3398: 3392: 3385: 3384: 3376: 3368: 3364: 3359: 3354: 3350: 3346: 3342: 3338: 3334: 3327: 3320: 3315: 3308: 3304: 3301: 3295: 3280: 3276: 3269: 3261: 3255: 3251: 3250: 3242: 3234: 3228: 3224: 3217: 3209: 3203: 3199: 3192: 3184: 3177: 3169: 3163: 3159: 3152: 3144: 3140: 3136: 3132: 3128: 3124: 3117: 3109: 3103: 3099: 3092: 3086: 3081: 3073: 3067: 3063: 3056: 3048: 3042: 3038: 3034: 3028: 3019: 3014: 3010: 3006: 3002: 2998: 2992: 2984: 2980: 2974: 2966: 2962: 2956: 2948: 2944: 2940: 2936: 2929: 2921: 2915: 2911: 2907: 2903: 2897: 2889: 2885: 2880: 2875: 2871: 2867: 2863: 2856: 2848: 2842: 2832: 2831: 2823: 2808: 2804: 2797: 2790: 2784: 2776: 2775: 2767: 2761: 2756: 2749:(3): 493–530. 2748: 2744: 2740: 2733: 2726: 2722: 2718: 2714: 2711: 2705: 2697: 2693: 2688: 2683: 2679: 2675: 2671: 2667: 2663: 2656: 2648: 2644: 2640: 2636: 2632: 2628: 2621: 2613: 2609: 2605: 2601: 2597: 2593: 2586: 2578: 2574: 2570: 2566: 2559: 2548: 2544: 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1756: 1754: 1751: 1749: 1746: 1744: 1741: 1739: 1736: 1734: 1731: 1730: 1721: 1717: 1713: 1709: 1704: 1701: 1697: 1696: 1686: 1681: 1677: 1674: 1671: 1668: 1665: 1662: 1661: 1660: 1658: 1645: 1641: 1637: 1634: 1631: 1628: 1625: 1622: 1618: 1614: 1611: 1608: 1604: 1600: 1597: 1596: 1595: 1593: 1592:metaheuristic 1580: 1576: 1572: 1569: 1565: 1562: 1561: 1560: 1558: 1552: 1538: 1535: 1531: 1527: 1522: 1518: 1514: 1510: 1507: 1504: 1500: 1496: 1492: 1488: 1484: 1480: 1476: 1472: 1468: 1465: 1462: 1459: 1456: 1453: 1450: 1446: 1443: 1442: 1441: 1439: 1433: 1422: 1419: 1411: 1401: 1397: 1391: 1390: 1385:This section 1383: 1379: 1374: 1373: 1358: 1355: 1353: 1350: 1348: 1345: 1343: 1340: 1338: 1335: 1334: 1333: 1328:Parent fields 1324: 1314: 1312: 1308: 1303: 1299: 1295: 1285: 1283: 1279: 1275: 1271: 1267: 1263: 1259: 1254: 1250: 1246: 1242: 1238: 1234: 1229: 1227: 1222: 1217: 1213: 1209: 1205: 1201: 1197: 1184:Steven Skiena 1180: 1178: 1172: 1170: 1165: 1163: 1159: 1154: 1150: 1148: 1144: 1140: 1139:hill climbing 1136: 1132: 1128: 1124: 1119: 1117: 1113: 1109: 1099: 1095: 1092: 1090: 1084: 1082: 1076: 1074: 1070: 1066: 1062: 1058: 1054: 1050: 1046: 1042: 1038: 1034: 1029: 1020: 1018: 1013: 1004: 1002: 992: 988: 986: 980: 978: 977:Hamming walls 974: 969: 967: 963: 959: 955: 950: 946: 942: 938: 934: 930: 924: 906: 902: 898: 894: 890: 889:hill climbing 886: 882: 878: 874: 869: 866: 862: 859: 855: 851: 847: 842: 839: 835: 831: 826: 822: 818: 814: 810: 807: 803: 800: 796: 791: 786: 782: 781: 780: 771: 768: 760: 750: 746: 740: 739: 734:This section 732: 728: 723: 722: 714: 712: 704: 703: 699: 695: 694: 693: 687: 684: 680: 679: 678: 666: 663: 660: 657: 654: 651: 650: 649: 641: 639: 635: 625: 623: 619: 615: 614:genetic drift 611: 608:probability, 607: 602: 599: 597: 592: 588: 584: 582: 578: 574: 568: 564: 554: 552: 548: 544: 540: 535: 533: 528: 524: 519: 517: 513: 509: 508:fitness-based 505: 499: 489: 487: 476: 472: 470: 466: 462: 458: 454: 450: 447:(also called 446: 445:array 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