Evolutionary computation

Evolution of a population of random images. Each frame in the animation is a generation showing the best fitness individual with a genome made up of the greyscale level of each patch. Evolution repeatedly follows 1. evaluate fitness, 2. rank individuals and 3. include some genes from next highest fitness individual. Fitness is the error difference with an image of Charles Darwin.

Part of a series on the
Evolutionary algorithm
Chromosome Fitness function Genetic operator Crossover Mutation Selection Population model
Genetic algorithm (GA)
Chromosome Clonal selection algorithm Fly algorithm Genetic fuzzy systems Genetic memory Schema Promoter based GA
Genetic programming (GP)
Cartesian GP Linear GP Gene expression programming Grammatical evolution Multi expression programming
Differential evolution
Evolution strategy
Evolutionary programming
Related topics
Cellular EA Cultural algorithm Effective fitness Evolutionary computation Gaussian adaptation Grammar induction Evolutionary multimodal optimization Memetic algorithm Neuroevolution
. This confirms the initial result about undecidability of natural evolution and evolutionary algorithms and processes. Evolutionary finite automata, the simplest subclass of Evolutionary automata working in terminal mode can accept arbitrary languages over a given alphabet, including non-recursively enumerable (e.g., diagonalization language) and recursively enumerable but not recursive languages (e.g., language of the universal Turing machine)De Jong, Kenneth A. (2006). Evolutionary Computation: A Unified Approach. Cambridge, Mass.: MIT Press. ISBN 978-0-262-52960-0. ↑ Eiben, A. E.; Smith, J. E. (2015), Evolutionary Computing: The Origins, Natural Computing Series, Berlin, Heidelberg: Springer, pp. 13–24, doi:10.1007/978-3-662-44874-8_2, ISBN 978-3-662-44873-1 ↑ Fischer, Thomas (1986), "Kybernetische Systemanalyse einer Tuchfabrik zur Einführung eines computergestützten Dispositionssystems der Fertigung", DGOR, Berlin, Heidelberg: Springer, p. 120, doi:10.1007/978-3-642-71161-9_14, ISBN 978-3-642-71162-6 ↑ Fraser AS (1958). "Monte Carlo analyses of genetic models". Nature. 181 (4603): 208–9. Bibcode:1958Natur.181..208F. doi:10.1038/181208a0. PMID 13504138. S2CID 4211563. ↑ 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. Bibcode:2003RSPTA.361.1781J. doi:10.1098/rsta.2003.1225. PMID 12952685. S2CID 34259612. ↑ "Biological Information". The Stanford Encyclopedia of Philosophy. Metaphysics Research Lab, Stanford University. 2016. ↑ Burgin, Mark; Eberbach, Eugene (2013). "Recursively Generated Evolutionary Turing Machines and Evolutionary Automata". In Xin-She Yang (ed.). Artificial Intelligence, Evolutionary Computing and Metaheuristics. Studies in Computational Intelligence. Vol. 427. Berlin, Heidelberg: Springer. pp. 201–230. doi:10.1007/978-3-642-29694-9_9. ISBN 978-3-642-29693-2. ↑ Eberbach, Eugene; Burgin, Mark (2002). "On Expressiveness of Evolutionary Computation: Is EC Algorithmic?". World Congress on Computational Intelligence WCCI’2002. pp. 564–569. doi:10.1109/CEC.2002.1006988. ↑ Hopcroft, J.E.; Motwani, R.; Ullman, J.D. (2001). Introduction to Automata Theory, Languages, and Computation. Boston, San Francisco, New York: Addison Wesley. ISBN 9780321210296. ↑ ISBN 978-1558605107. S. Cagnoni, et al. Real-World Applications of Evolutionary Computing. Berlin: Springer, 2000, LNCS series, vol. 1803, ISBN 0-262-52960-2. A. E. Eiben and J.E. Smith. From Evolutionary Computation to the Evolution of Things, Nature, 521:476-482, 2015, doi:10.1038/nature14544. A. E. Eiben and J.E. Smith. Introduction to Evolutionary Computing. 2nd ed., Berlin, Heidelberg: Springer, 2015, ISBN 9780471265160, doi:10.1109/9780470544600.ch7. D. E. Goldberg. Genetic Algorithms in Search, Optimization and Machine Learning. Boston, MA: Addison Wesley, 1989, ISBN 3540741097, doi:10.1007/978-3-540-74111-4. J. R. Koza. Genetic Programming: On the Programming of Computers by means of Natural Evolution. Massachusetts: MIT Press, 1992, ISBN 978-3-540-60676-5, doi:10.1007/978-3-662-03315-9. Z. Michalewicz and D.B. Fogel. How to Solve It: Modern Heuristics, Springer, 2004, ISBN 3-7728-0373-3. H.-P. Schwefel. Numerical Optimization of Computer Models. John New-York: Wiley & Sons, 1981. 1995 – 2nd ed., Y. Zhang; S. Li. (2017). "PSA: A novel optimization algorithm based on survival rules of porcellio scaber". arXiv:1709.09840 [cs.NE]. External links edit Article in the Stanford Encyclopedia of Philosophy about Biological Information (English) Evolutionary computation
Main Topics	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) Cultural algorithm 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	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 Premature convergence Program synthesis
Organizations	ACM IEEE ACM SIGEVO IEEE CIS
Conferences	CEC GECCO PPSN EvoStar FOGA
Journals	Evolutionary Computation (journal) IEEE Trans Evol Comput ACM Trans Evol Learning Optim