Evolutionary computation
Part of a series on |
Evolutionary biology |
---|
In
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 as well as, depending on the method, mixing parental information. In biological terminology, a
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 concept of mimicking evolutionary processes to solve problems originates before the advent of computers, such as when
Evolutionary computing as a field began in earnest in the 1950s and 1960s.[1] There were several independent attempts to use the process of evolution in computing at this time, which developed separately for roughly 15 years. Three branches emerged in different places to attain this goal: evolution strategies, evolutionary programming, and genetic algorithms. A fourth branch, genetic programming, eventually emerged in the early 1990s. These approaches differ in the method of selection, the permitted mutations, and the representation of genetic data. By the 1990s, the distinctions between the historic branches had begun to blur, and the term 'evolutionary computing' was coined in 1991 to denote a field that exists over all four paradigms.[3]
In 1962, Lawrence J. Fogel initiated the research of Evolutionary Programming in the United States, which was considered an artificial intelligence endeavor. In this system, finite state machines are used to solve a prediction problem: these machines would be mutated (adding or deleting states, or changing the state transition rules), and the best of these mutated machines would be evolved further in future generations. The final finite state machine may be used to generate predictions when needed. The evolutionary programming method was successfully applied to prediction problems, system identification, and automatic control. It was eventually extended to handle time series data and to model the evolution of gaming strategies.[3]
In 1964, Ingo Rechenberg and Hans-Paul Schwefel introduce the paradigm of evolution strategies in Germany.[3] Since traditional gradient descent techniques produce results that may get stuck in local minima, Rechenberg and Schwefel proposed that random mutations (applied to all parameters of some solution vector) may be used to escape these minima. Child solutions were generated from parent solutions, and the more successful of the two was kept for future generations. This technique was first used by the two to successfully solve optimization problems in fluid dynamics.[4] Initially, this optimization technique was performed without computers, instead relying on dice to determine random mutations. By 1965, the calculations were performed wholly by machine.[3]
John Henry Holland introduced genetic algorithms in the 1960s, and it was further developed at the University of Michigan in the 1970s.[5] While the other approaches were focused on solving problems, Holland primarily aimed to use genetic algorithms to study adaptation and determine how it may be simulated. Populations of chromosomes, represented as bit strings, were transformed by an artificial selection process, selecting for specific 'allele' bits in the bit string. Among other mutation methods, interactions between chromosomes were used to simulate the recombination of DNA between different organisms. While previous methods only tracked a single optimal organism at a time (having children compete with parents), Holland's genetic algorithms tracked large populations (having many organisms compete each generation).
By the 1990s, a new approach to evolutionary computation that came to be called genetic programming emerged, advocated for by John Koza among others.[3] In this class of algorithms, the subject of evolution was itself a program written in a high-level programming language (there had been some previous attempts as early as 1958 to use machine code, but they met with little success). For Koza, the programs were Lisp S-expressions, which can be thought of as trees of sub-expressions. This representation permits programs to swap subtrees, representing a sort of genetic mixing. Programs are scored based on how well they complete a certain task, and the score is used for artificial selection. Sequence induction, pattern recognition, and planning were all successful applications of the genetic programming paradigm.
Many other figures played a role in the history of evolutionary computing, although their work did not always fit into one of the major historical branches of the field. The earliest computational simulations of
Techniques
Evolutionary computing techniques mostly involve metaheuristic optimization algorithms. Broadly speaking, the field includes:
- Agent-based modeling
- Ant colony optimization
- Artificial immune systems
- Artificial life (also see digital organism)
- Cultural algorithms
- Coevolutionary algorithm
- Differential evolution
- Dual-phase evolution
- Estimation of distribution algorithm
- Evolutionary algorithm
- Evolutionary programming
- Evolution strategy
- Gene expression programming
- Genetic algorithm
- Genetic programming
- Grammatical evolution
- Learnable evolution model
- Learning classifier system
- Memetic algorithms
- Neuroevolution
- Particle swarm optimization
- Beetle antennae search
- Self-organization such as self-organizing maps, competitive learning
- Swarm intelligence
A through catalogue with many other recently proposed algorithms has been published in the Evolutionary Computation Bestiary.[11] It is important to note that many recent algorithms, however, have poor experimental validation.[12]
Evolutionary algorithms
In this process, there are two main forces that form the basis of evolutionary systems: Recombination (e.g. crossover) 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.
Evolutionary algorithms and biology
However, the use of algorithms and informatics, in particular of
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.[13] 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.[14]
Furthermore, following concepts from
The analogy to computation extends also to the relationship between
Evolutionary automata[16][17][18], a generalization of Evolutionary Turing machines[19][20], 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[18][21]. 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)[22].
Notable practitioners
The list of active researchers is naturally dynamic and non-exhaustive. A network analysis of the community was published in 2007.[23]
- Kalyanmoy Deb
- Kenneth A De Jong
- Peter J. Fleming
- David B. Fogel
- Stephanie Forrest
- David E. Goldberg
- John Henry Holland
- Theo Jansen
- John Koza
- Zbigniew Michalewicz
- Melanie Mitchell
- Peter Nordin
- Riccardo Poli
- Ingo Rechenberg
- Hans-Paul Schwefel
Journals
While articles on or using evolutionary computation permeate the literature, several journals are dedicated to evolutionary computation:
- Evolutionary Computation (journal) (founded 1993)
- Artificial Life (journal) (founded 1993)
- IEEE Transactions on Evolutionary Computation (founded 1997)
- Genetic Programming and Evolvable Machines (founded 2000)
- Swarm Intelligence(founded 2007)
- Evolutionary Intelligence (founded 2008)
- Journal of Artificial Evolution and Applications (2008-2010)
- Memetic Computing(founded 2009)
- International Journal of Applied Evolutionary Computation (founded 2010)
- Swarm and Evolutionary Computation (founded 2011)
- International Journal of Swarm Intelligence and Evolutionary Computation(founded 2012)
Conferences
The main conferences in the evolutionary computation area include
- ACM Genetic and Evolutionary Computation Conference (GECCO),
- IEEE Congress on Evolutionary Computation (CEC),
- EvoStar, which comprises four conferences: EuroGP, EvoApplications, EvoCOP and EvoMUSART,
- Parallel Problem Solving from Nature (PPSN).
See also
- 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
- Unconventional computing
- Universal Darwinism
External links
Bibliography
- Th. Bäck, D.B. Fogel, and ISBN 0750303921
- Th. Bäck and H.-P. Schwefel. An overview of evolutionary algorithms for parameter optimization. Archived July 12, 2018, at the Wayback Machine 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, ISBN 3642234232
- K. A. De Jong, Evolutionary computation: a unified approach. MIT Press, Cambridge MA, 2006
- A. E. Eiben and J.E. Smith, From evolutionary computation to the evolution of things, Nature, 521:476-482, doi:10.1038/nature14544, 2015
- A. E. Eiben and J.E. Smith, Introduction to Evolutionary Computing, Springer, First edition, 2003; Second edition, 2015
- 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 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, ISBN 3642088929
- ISBN 3540606769
- 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 Archived March 10, 2014, at the Wayback Machine. Wiley, 2013.
- M. Sipper; W. Fu; K. Ahuja; J. H. Moore (2018). "Investigating the parameter space of evolutionary algorithms". BioData Mining. 11: 2. PMID 29467825.
- Y. Zhang; S. Li. (2017). "PSA: A novel optimization algorithm based on survival rules of porcellio scaber". arXiv:1709.09840 [cs.NE].
References
- ^ ISBN 978-3-662-44873-1, retrieved May 6, 2022
- ].
- ^ )
- ISBN 978-3-642-71162-6, retrieved May 6, 2022
- ISBN 978-0-262-28001-3.
- ^ Barricelli, Nils Aall (1954). "Esempi Numerici di processi di evoluzione". Methodos: 45–68.
- S2CID 4211563.
- ISBN 978-0-262-11170-6.
- ISBN 9783540201670. Retrieved September 17, 2016.
- S2CID 34259612.
- )
- S2CID 254616518.
- ^ "Biological Information". The Stanford Encyclopedia of Philosophy. Metaphysics Research Lab, Stanford University. 2016.
- S2CID 22624633.
- PMID 19016882.
- ISBN 978-3-642-29693-2.
- ^ 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
- ^ .
- ^ 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.
- ^ Eberbach, E. (2005) Toward a theory of evolutionary computation, BioSystems, v. 82, pp. 1-19.
- S2CID 2869386.
- ^ Hopcroft, J.E., R. Motwani, and J.D. Ullman (2001) Introduction to Automata Theory, Languages, and Computation, Addison Wesley, Boston/San Francisco/New York
- ].