Evolutionary Computation
Get Evolutionary Computation essential facts below. View Videos or join the Evolutionary Computation discussion. Add Evolutionary Computation to your PopFlock.com topic list for future reference or share this resource on social media.
Evolutionary Computation

In computer science, evolutionary computation is a family of algorithms for global optimization inspired by biological evolution, and the subfield of artificial intelligence and soft computing studying these algorithms. In technical terms, they are a family of population-based trial and error problem solvers with a metaheuristic or stochastic optimization character.

In evolutionary computation, an initial set of candidate solutions is generated and iteratively updated. Each new generation is produced by stochastically removing less desired solutions, and introducing small random changes. In biological terminology, a population of solutions is subjected to natural selection (or artificial selection) and mutation. As a result, the population will gradually evolve to increase in fitness, in this case the chosen fitness function of the algorithm.

Evolutionary computation techniques can produce highly optimized solutions in a wide range of problem settings, making them popular in computer science. Many variants and extensions exist, suited to more specific families of problems and data structures. Evolutionary computation is also sometimes used in evolutionary biology as an in silico experimental procedure to study common aspects of general evolutionary processes.

History

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 the evolutionary computation.

These terminologies denote the field of evolutionary computing and consider evolutionary programming, evolution strategies, genetic algorithms, and genetic programming as sub-areas.

The earliest computational simulations of evolution using evolutionary algorithms and artificial life techniques were performed by Nils Aall Barricelli in 1953,[1] with first results published in 1954.[2] Another pioneer in the 1950s was Alex Fraser, who published a series of papers on simulation of artificial selection.[3] 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.[4] Genetic algorithms in particular became popular through the writing of John Holland.[5] As academic interest grew, dramatic increases in the power of computers allowed practical applications, including the automatic evolution of computer programs.[6] 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.[7][8]

Techniques

Evolutionary computing techniques mostly involve metaheuristic optimization algorithms. Broadly speaking, the field includes:

Evolutionary algorithms

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 mutation and crossover 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.

Evolutionary algorithms and biology

Genetic algorithms deliver methods to model biological systems and systems biology that are linked to the theory of dynamical systems, since they are used to predict the future states of the system. This is just a vivid (but perhaps misleading) way of drawing attention to the orderly, well-controlled and highly structured character of development in biology.

However, the use of algorithms and informatics, in particular of computational theory, beyond the analogy to dynamical systems, is also relevant to understand evolution itself.

This view has the merit of recognizing that there is no central control of development; organisms develop as a result of local interactions within and between cells. The most promising ideas about program-development parallels seem to us to be ones that point to an apparently close analogy between processes within cells, and the low-level operation of modern computers.[9] Thus, biological systems are like computational machines that process input information to compute next states, such that biological systems are closer to a computation than classical dynamical system.[10]

Furthermore, following concepts from computational theory, micro processes in biological organisms are fundamentally incomplete and undecidable (completeness (logic)), implying that "there is more than a crude metaphor behind the analogy between cells and computers.[11]

The analogy to computation extends also to the relationship between inheritance systems and biological structure, which is often thought to reveal one of the most pressing problems in explaining the origins of life.

Evolutionary automata[12][13][14], a generalization of Evolutionary Turing machines[15][16], have been introduced in order to investigate more precisely properties of biological and evolutionary computation. In particular, they allow to obtain new results on expressiveness of evolutionary computation[14][17]. 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)[18].

Notable practitioners

The list of active researchers is naturally dynamic and non-exhaustive. A network analysis of the community was published in 2007.[19]

Conferences

The main conferences in the evolutionary computation area include

See also

External links

Bibliography

References

  1. ^ Taylor, Tim; Dorin, Alan (2020). Rise of the Self-Replicators: Early Visions of Machines, AI and Robots That Can Reproduce and Evolve. Cham: Springer International Publishing. doi:10.1007/978-3-030-48234-3. ISBN 978-3-030-48233-6. S2CID 220855726. Lay summary.
  2. ^ Barricelli, Nils Aall (1954). "Esempi Numerici di processi di evoluzione". Methodos: 45-68.
  3. ^ 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.
  4. ^ Rechenberg, Ingo (1973). Evolutionsstrategie - Optimierung technischer Systeme nach Prinzipien der biologischen Evolution (PhD thesis) (in German). Fromman-Holzboog.
  5. ^ Holland, John H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press. ISBN 978-0-262-58111-0.
  6. ^ Koza, John R. (1992). Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press. ISBN 978-0-262-11170-6.
  7. ^ G. C. Onwubolu and B V Babu, Onwubolu, Godfrey C.; Babu, B. V. (January 21, 2004). New Optimization Techniques in Engineering. ISBN 9783540201670. Retrieved 2016.
  8. ^ 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.
  9. ^ "Biological Information". The Stanford Encyclopedia of Philosophy. Metaphysics Research Lab, Stanford University. 2016.
  10. ^ J.G. Diaz Ochoa (2018). "Elastic Multi-scale Mechanisms: Computation and Biological Evolution". Journal of Molecular Evolution. 86 (1): 47-57. Bibcode:2018JMolE..86...47D. doi:10.1007/s00239-017-9823-7. PMID 29248946. S2CID 22624633.
  11. ^ A. Danchin (2008). "Bacteria as computers making computers". FEMS Microbiol. Rev. 33 (1): 3-26. doi:10.1111/j.1574-6976.2008.00137.x. PMC 2704931. PMID 19016882.
  12. ^ 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. 427. Springer-Verlag. pp. 201-230. doi:10.1007/978-3-642-29694-9_9. ISBN 978-3-642-29693-2.
  13. ^ Burgin, M. and Eberbach, E. (2010) Bounded and Periodic Evolutionary Machines, in Proc. 2010 Congress on Evolutionary Computation (CEC'2010), Barcelona, Spain, 2010, pp. 1379-1386
  14. ^ a b Burgin, M.; Eberbach, E. (2012). "Evolutionary Automata: Expressiveness and Convergence of Evolutionary Computation". The Computer Journal. 55 (9): 1023-1029. doi:10.1093/comjnl/bxr099.
  15. ^ Eberbach E. (2002) On Expressiveness of Evolutionary Computation: Is EC Algorithmic?, Proc. 2002 World Congress on Computational Intelligence WCCI'2002, Honolulu, HI, 2002, 564-569.
  16. ^ Eberbach, E. (2005) Toward a theory of evolutionary computation, BioSystems, v. 82, pp. 1-19.
  17. ^ Eberbach, Eugene; Burgin, Mark (2009). "Evolutionary automata as foundation of evolutionary computation: Larry Fogel was right". 2009 IEEE Congress on Evolutionary Computation. IEEE. pp. 2149-2156. doi:10.1109/CEC.2009.4983207. ISBN 978-1-4244-2958-5. S2CID 2869386.
  18. ^ Hopcroft, J.E., R. Motwani, and J.D. Ullman (2001) Introduction to Automata Theory, Languages, and Computation, Addison Wesley, Boston/San Francisco/New York
  19. ^ 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 [cs.CY].




  This article uses material from the Wikipedia page available here. It is released under the Creative Commons Attribution-Share-Alike License 3.0.

Evolutionary_computation
 



 



 
Music Scenes