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In artificial intelligence, an evolutionary algorithm (EA) is a subset of evolutionary computation, a generic population-based metaheuristic optimization algorithm. An EA uses mechanisms inspired by biological evolution, such as reproduction, mutation, recombination, and selection. Candidate solutions to the optimization problem play the role of individuals in a population, and the fitness function determines the quality of the solutions (see also loss function). Evolution of the population then takes place after the repeated application of the above operators. ''Artificial evolution'' (AE) describes a process involving individual ''evolutionary algorithm''s; EAs are individual components that participate in an AE. Evolutionary algorithms often perform well approximating solutions to all types of problems because they ideally do not make any assumption about the underlying fitness landscape; this generality is shown by successes in fields as diverse as engineering, art, biology, economics, marketing, genetics, operations research, robotics, social sciences, physics, politics and chemistry. Techniques from evolutionary algorithms applied to the modeling of biological evolution are generally limited to explorations of microevolutionary processes and planning models based upon cellular processes. The computer simulations ''Tierra'' and ''Avida'' attempt to model macroevolutionary dynamics. In most real applications of EAs, computational complexity is a prohibiting factor. In fact, this computational complexity is due to fitness function evaluation. Fitness approximation is one of the solutions to overcome this difficulty. However, seemingly simple EA can solve often complex problems; therefore, there may be no direct link between algorithm complexity and problem complexity. A possible limitation of many evolutionary algorithms is their lack of a clear genotype-phenotype distinction. In nature, the fertilized egg cell undergoes a complex process known as embryogenesis to become a mature phenotype. This indirect encoding is believed to make the genetic search more robust (i.e. reduce the probability of fatal mutations), and also may improve the evolvability of the organism.〔G.S. Hornby and J.B. Pollack. Creating high-level components with a generative representation for body-brain evolution. Artificial Life, 8(3):223–246, 2002.〕〔Jeff Clune, Benjamin Beckmann, Charles Ofria, and Robert Pennock. ("Evolving Coordinated Quadruped Gaits with the HyperNEAT Generative Encoding" ). ''Proceedings of the IEEE Congress on Evolutionary Computing Special Section on Evolutionary Robotics'', 2009. Trondheim, Norway.〕 Such indirect (aka generative or developmental) encodings also enable evolution to exploit the regularity in the environment.〔J. Clune, C. Ofria, and R. T. Pennock, “How a generative encoding fares as problem-regularity decreases,” in PPSN (G. Rudolph, T. Jansen, S. M. Lucas, C. Poloni, and N. Beume, eds.), vol. 5199 of Lecture Notes in Computer Science, pp. 358–367, Springer, 2008.〕 Recent work in the field of artificial embryogeny, or artificial developmental systems, seeks to address these concerns. And gene expression programming successfully explores a genotype-phenotype system, where the genotype consists of linear multigenic chromosomes of fixed length and the phenotype consists of multiple expression trees or computer programs of different sizes and shapes.〔(Ferreira, C., 2001. Gene Expression Programming: A New Adaptive Algorithm for Solving Problems. Complex Systems, Vol. 13, issue 2: 87-129. )〕 ==Implementation of biological processes== # Generate the initial population of individuals randomly - first generation # Evaluate the fitness of each individual in that population # Repeat on this generation until termination (time limit, sufficient fitness achieved, etc.): ## Select the best-fit individuals for reproduction - parents ## Breed new individuals through crossover and mutation operations to give birth to offspring ## Evaluate the individual fitness of new individuals ## Replace least-fit population with new individuals 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Evolutionary algorithm」の詳細全文を読む スポンサード リンク
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