Contents 1 History 2 Techniques 3 Evolutionary algorithms 4 Practitioners 5 See also 6 Bibliography 7 References


History[edit] The use of Evolutionary principles for automated problem solving originated in the 1950s. It was not until the 1960s that three distinct interpretations of this idea started to be developed in three different places. Evolutionary programming was introduced by Lawrence J. Fogel in the US, while John Henry Holland called his method a genetic algorithm. In Germany Ingo Rechenberg and Hans-Paul Schwefel introduced evolution strategies. These areas developed separately for about 15 years. From the early nineties on they are unified as different representatives ("dialects") of one technology, called evolutionary computing. Also in the early nineties, a fourth stream following the general ideas had emerged – genetic programming. Since the 1990s, nature-inspired algorithms are becoming an increasingly significant part of evolutionary computation. These terminologies denote the field of evolutionary computing and consider evolutionary programming, evolution strategies, genetic algorithms, and genetic programming as sub-areas. Simulations of evolution using evolutionary algorithms and artificial life started with the work of Nils Aall Barricelli in the 1960s, and was extended by Alex Fraser, who published a series of papers on simulation of artificial selection.[1] Artificial evolution became a widely recognised optimisation method as a result of the work of Ingo Rechenberg in the 1960s and early 1970s, who used evolution strategies to solve complex engineering problems.[2] Genetic algorithms in particular became popular through the writing of John Holland.[3] As academic interest grew, dramatic increases in the power of computers allowed practical applications, including the automatic evolution of computer programs.[4] Evolutionary algorithms are now used to solve multi-dimensional problems more efficiently than software produced by human designers, and also to optimise the design of systems.[5][6]


Techniques[edit] Evolutionary computing techniques mostly involve metaheuristic optimization algorithms. Broadly speaking, the field includes: Ant colony optimization Artificial immune systems Artificial life (also see digital organism) Cultural algorithms Differential evolution Dual-phase evolution Estimation of distribution algorithms Evolutionary algorithms Evolutionary programming Evolution strategy Gene expression programming Genetic algorithm Genetic programming Grammatical evolution Learnable evolution model Learning classifier systems Memetic algorithms Particle swarm optimization Self-organization such as self-organizing maps, competitive learning Swarm intelligence


Evolutionary algorithms[edit] Main article: Evolutionary algorithm Evolutionary algorithms form a subset of evolutionary computation in that they generally only involve techniques implementing mechanisms inspired by biological evolution such as reproduction, mutation, recombination, natural selection and survival of the fittest. Candidate solutions to the optimization problem play the role of individuals in a population, and the cost function determines the environment within which the solutions "live" (see also fitness function). Evolution of the population then takes place after the repeated application of the above operators. In this process, there are two main forces that form the basis of evolutionary systems: Recombination and mutation create the necessary diversity and thereby facilitate novelty, while selection acts as a force increasing quality. Many aspects of such an evolutionary process are stochastic. Changed pieces of information due to recombination and mutation are randomly chosen. On the other hand, selection operators can be either deterministic, or stochastic. In the latter case, individuals with a higher fitness have a higher chance to be selected than individuals with a lower fitness, but typically even the weak individuals have a chance to become a parent or to survive.


Practitioners[edit] The list of active researchers is naturally dynamic and non-exhaustive. A network analysis of the community was published in 2007.[7] Thomas Bäck Wolfgang Banzhaf B V Babu Kalyanmoy Deb Kenneth A De Jong Gusz Eiben Peter J. Fleming David B. Fogel Stephanie Forrest David E. Goldberg Emma Hart John Henry Holland Theo Jansen John Koza Zbigniew Michalewicz Peter Nordin Riccardo Poli Ingo Rechenberg Marc Schoenauer Hans-Paul Schwefel Jim Smith Gloria Townsend Xin Yao


See also[edit] Artificial intelligence portal Computer Science portal Adaptive dimensional search Artificial development Autoconstructive Developmental biology Digital organism Estimation of distribution algorithm Evolutionary robotics Evolved antenna Fitness approximation Fitness function Fitness landscape Genetic operators Grammatical evolution Human-based evolutionary computation Inferential programming Interactive evolutionary computation List of digital organism simulators Mutation testing No free lunch in search and optimization Program synthesis Test functions for optimization Universal Darwinism


Bibliography[edit] Th. Bäck, D.B. Fogel, and Z. Michalewicz (Editors), Handbook of Evolutionary Computation, 1997, ISBN 0750303921 Th. Bäck and H.-P. Schwefel. An overview of evolutionary algorithms for parameter optimization. Evolutionary Computation, 1(1):1–23, 1993. W. Banzhaf, P. Nordin, R.E. Keller, and F.D. Francone. Genetic Programming — An Introduction. Morgan Kaufmann, 1998. S. Cagnoni, et al., Real-World Applications of Evolutionary Computing, Springer-Verlag Lecture Notes in Computer Science, Berlin, 2000. R. Chiong, Th. Weise, Z. Michalewicz (Editors), Variants of Evolutionary Algorithms for Real-World Applications, Springer, 2012, ISBN 3642234232 K. A. De Jong, Evolutionary computation: a unified approach. MIT Press, Cambridge MA, 2006 A. E. Eiben and M. Schoenauer, Evolutionary computing, Information Processing Letters, 82(1): 1–6, 2002. A. E. Eiben and J.E. Smith, Introduction to Evolutionary Computing, Springer, First edition, 2003, ISBN 3-540-40184-9, D. B. Fogel. Evolutionary Computation. Toward a New Philosophy of Machine Intelligence. IEEE Press, Piscataway, NJ, 1995. L. J. Fogel, A. J. Owens, and M. J. Walsh. Artificial Intelligence through Simulated Evolution. New York: John Wiley, 1966. D. E. Goldberg. Genetic algorithms in search, optimization and machine learning. Addison Wesley, 1989. J. H. Holland. Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor, 1975. P. Hingston, L. Barone, and Z. Michalewicz (Editors), Design by Evolution, Natural Computing Series, 2008, Springer, ISBN 3540741097 J. R. Koza. Genetic Programming: On the Programming of Computers by means of Natural Evolution. MIT Press, Massachusetts, 1992. F.J. Lobo, C.F. Lima, Z. Michalewicz (Editors), Parameter Setting in Evolutionary Algorithms, Springer, 2010, ISBN 3642088929 Z. Michalewicz, Genetic Algorithms + Data Structures – Evolution Programs, 1996, Springer, ISBN 3540606769 Z. Michalewicz and D.B. Fogel, How to Solve It: Modern Heuristics, Springer, 2004, ISBN 978-3-540-22494-5 I. Rechenberg. Evolutionstrategie: Optimierung Technischer Systeme nach Prinzipien des Biologischen Evolution. Fromman-Hozlboog Verlag, Stuttgart, 1973. (in German) H.-P. Schwefel. Numerical Optimization of Computer Models. John Wiley & Sons, New-York, 1981. 1995 – 2nd edition. D. Simon. Evolutionary Optimization Algorithms. Wiley, 2013. Y. Zhang and S. Li. "PSA: A novel optimization algorithm based on survival rules of porcellio scaber".


References[edit] ^ Fraser AS (1958). "Monte Carlo analyses of genetic models". Nature. 181 (4603): 208–9. doi:10.1038/181208a0. PMID 13504138.  ^ Rechenberg, Ingo (1973). Evolutionsstrategie – Optimierung technischer Systeme nach Prinzipien der biologischen Evolution (PhD thesis) (in German). Fromman-Holzboog.  ^ Holland, John H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press. ISBN 0-262-58111-6.  ^ Koza, John R. (1992). Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press. ISBN 0-262-11170-5.  ^ G. C. Onwubolu and B V Babu, "New Optimization Techniques in Engineering". Retrieved 17 September 2016.  ^ Jamshidi M (2003). "Tools for intelligent control: fuzzy controllers, neural networks and genetic algorithms". Philosophical Transactions of the Royal Society A. 361 (1809): 1781–808. doi:10.1098/rsta.2003.1225. PMID 12952685.  ^ J.J. Merelo and C. Cotta (2007). "Who is the best connected EC researcher? Centrality analysis of the complex network of authors in evolutionary computation". arXiv:0708.2021 .  v t e Evolutionary computation Main Topics Convergence (evolutionary computing) Evolutionary algorithm Evolutionary data mining Evolutionary multimodal optimization Human-based evolutionary computation Interactive evolutionary computation Algorithms Cellular evolutionary algorithm Covariance Matrix Adaptation Evolution Strategy (CMA-ES) Differential evolution Evolutionary programming Genetic algorithm Genetic programming Gene expression programming Evolution strategy Natural evolution strategy Neuroevolution Learning classifier system Related techniques Swarm intelligence Ant colony optimization Bees algorithm Cuckoo search Particle swarm optimization Bacterial Colony Optimization Metaheuristic methods Grey Wolf Optimizer Firefly algorithm Harmony search Gaussian adaptation Memetic algorithm Related topics Artificial development Artificial intelligence Artificial life Digital organism Evolutionary robotics Fitness function Fitness landscape Fitness approximation Genetic operators Interactive evolutionary computation No free lunch in search and optimization Machine learning Mating pool Program synthesis Journals Evolutionary Computation (journal) Retrieved from "https://en.wikipedia.org/w/index.php?title=Evolutionary_computation&oldid=816241412" Categories: Evolutionary computationEvolutionHidden categories: CS1 German-language sources (de)Use mdy dates from January 2012Articles with German-language external links


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