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With this encoding it is imposed that the information that is not expressed is still carried by the genotype in evolution but it is shielded from direct selective pressure, maintaining this way the diversity in the population, which has been a design premise for this algorithm. Therefore, a clear
340:(MDB) cognitive mechanism developed in the GII for real robots on-line learning. In another paper it is shown how the application of the PBGA together with an external memory that stores the successful obtained world models, is an optimal strategy for adaptation in dynamic environments.
291:(ANN) that are encoded into sequences of genes for constructing a basic ANN unit. Each of these blocks is preceded by a gene promoter acting as an on/off switch that determines if that particular unit will be expressed or not.
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and proceeded by an integer valued field that determines the promoter gene value and, consequently, the expression of the unit. By concatenating units of this type we can construct the whole network.
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difference is established between the search space and the solution space, permitting information learned and encoded into the genotypic representation to be preserved by disabling promoter genes.
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Recently, the PBGA has provided results that outperform other neuroevolutionary algorithms in non-stationary problems, where the fitness function varies in time.
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The PBGA was originally presented within the field of autonomous robotics, in particular in the real time learning of environment models of the robot.
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of a basic unit is a set of real valued weights followed by the parameters of the
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with all of its inbound connections as represented in the following figure:
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for neuroevolution developed by F. Bellas and R.J. Duro in the
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Covariance Matrix
Adaptation Evolution Strategy (CMA-ES)
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F. Bellas, A. Faiña, A. Prieto, and R.J. Duro (2006),
373:Modelling the world with statiscally neutral PBGAs
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397:F. Bellas, J.A. Becerra, R. J. Duro, (2009),
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285:Integrated Group for Engineering Research
707:No free lunch in search and optimization
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702:Interactive evolutionary computation
494:Interactive evolutionary computation
489:Human-based evolutionary computation
484:Evolutionary multimodal optimization
273:The promoter based genetic algorithm
88:Evolutionary multimodal optimization
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740:Evolutionary Computation (journal)
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299:The basic unit in the PBGA is a
113:Promoter based genetic algorithm
512:Cellular evolutionary algorithm
48:Cellular evolutionary algorithm
401:, Neurocomputing 72, 2134-2145
371:F. Bellas, R. J. Duro, (2002)
358:F. Bellas, R. J. Duro, (2002)
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608:Bacterial Colony Optimization
414:Grupo Integrado de IngenierĂa
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199:Cartesian genetic programming
118:Spiral optimization algorithm
336:It has been used inside the
214:Multi expression programming
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603:Particle swarm optimization
547:Gene expression programming
93:Particle swarm optimization
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761:Artificial neural networks
567:Learning classifier system
557:Natural evolution strategy
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289:artificial neural networks
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103:Natural evolution strategy
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424:Richard J. Duro’s website
419:Francisco Bellas’ website
771:Evolutionary computation
532:Evolutionary programming
479:Evolutionary data mining
460:Evolutionary computation
68:Evolutionary computation
766:Evolutionary algorithms
662:Artificial intelligence
588:Ant colony optimization
657:Artificial development
527:Differential evolution
474:Evolutionary algorithm
58:Differential evolution
38:Artificial development
29:Evolutionary algorithm
692:Fitness approximation
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171:Genetic fuzzy systems
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78:Gaussian adaptation
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73:Evolution strategy
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98:Memetic algorithm
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717:Mating pool
467:Main Topics
295:PBGA basics
755:Categories
504:Algorithms
347:References
146:Chromosome
176:Selection
156:Crossover
732:Journals
315:genotype
161:Mutation
21:a series
19:Part of
329:Results
279:) is a
229:Eurisko
319:neuron
301:neuron
224:Schema
23:on the
313:The
277:PBGA
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