Abstract
Recombination in the Genetic Algorithm (GA) is supposed to enable the component characteristics from two parents to be extracted and then reassembled in different combinations — hopefully producing an offspring that has the good characteristics of both parents. However, this can only work if it is possible to identify which parts of each parent should be extracted. Crossover in the standard GA takes subsets of genes that are adjacent on the genome. Other variations of the GA propose more sophisticated methods for identifying good subsets of genes within an individual. Our approach is different; rather than devising methods to enable successful extraction of gene-subsets from parents, we utilize variable-size individuals which represent subsets of genes from the outset. Joining together two individuals, creating an ‘offspring’ that is twice the size, straight-forwardly produces the sum of the parents’ characteristics. This form of component assembly is more closely analogous to combination of symbiotic organisms than it is to sexual recombination. Whereas sexual recombination, modeled by crossover, occurs between similar individuals and exchanges subsets of genes, symbiotic combination, as modeled in our operator, can occur between entirely unrelated species and combines together whole organisms. This paper summarizes our research on this approach to recombination in GAs and describes new methods that illustrate its potential.
Keywords
- Genetic Algorithm
- Pareto Optimization
- Partial Evaluation
- Uniform Crossover
- Sexual Recombination
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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References
Altenberg, L, 1995 “The Schema Theorem and Price’s Theorem”, FOGA3, editors Whitley & Vose, pp 23–49, Morgan Kaufmann, San Francisco.
Forrest, S & Mitchell, M, 1993, “What makes a problem hard for a Genetic Algorithm? Some anomalous results and their explanation” Machine Learning 13, pp.285–319.
Goldberg, DE, Deb, K, & Korb, B, 1989 “Messy Genetic Algorithms: Motivation, Analysis and first results”, Complex Systems, 3, 493–530.
Harik, GR, & Goldberg, DE, 1996, “Learning Linkage” in FOGA 4, Morgan Kaufmann, San Mateo, CA.
Holland, JH, 1975 “Adaptation in Natural and Artificial Systems”, Ann Arbor, Ml: The University of Michigan Press.
Horn, J, 1997, “Multicriteria Decision Making and Evolutionary Computation”, in Handbook of Evolutionary Computation, T. Back, D.B. Fogel, and Z. Michalewicz (eds.), IOP Press, NY.
Mahfoud, S, 1995, “Niching Methods for Genetic Algorithms”, PhD diss., Dept. General Engineering, University of Illinois. Also, IlliGAL Report No. 95001.
Maynard-Smith, J, and Szathmary, E, 1995 The Major Transition in Evolution, WH Freeman & Co.
Merezhkovsky KS, 1909 “The Theory of Two Plasms as the Basis of Symbiogenesis, a New Study or the Origins of Organisms,” Proceedings of the Studies of the Imperial Kazan University, Publishing Office of the Imperial University. (In Russian).
Moriarty, DE, 1997, Symbiotic Evolution of Neural Networks in Sequential Decision Tasks, PhD thesis, University of Texas at Austin, USA.
Potter, M, 1997, The Design and Analysis of a Computational Model of Cooperative Coevolulion, PhD thesis, George Mason University, Fairfax, Virginia.
Vekaria, K, & Clack, C, 1999, “Hitchhikers Get Around”, Artificial Evolution (EA) 1999, November 3–5, LIL, Universite du Littoral, Dunkerque, France.
Watson, RA, Hornby, GS & Pollack, JB, 1998, “Modeling Building-Block Interdependency”, PPSN V, proceedings of Fifth International Conference, Springer 1998, pp.97–106.
Watson, RA, & Pollack, JB, 1999a, “Incremental Commitment in Genetic Algorithms”, Proceedings of GECCO 1999. Banzhaf, et al. eds., Morgan Kaufmann, 710–717.
Watson, RA, & Pollack, JB, 1999b, “Hierarchically-Consistent Test Problems for Genetic Algorithms”, Proceedings of 1999 CEC. Angeline, et al. eds. IEEE Press, pp. 1406–1413.
Watson, RA, & Pollack, JB, 1999c, “How Symbiosis Can Guide Evolution”. Procs. of Fifth European Conference on Artificial Life, Floreano, D, Nicoud, JD, Mondada, F, eds., Springer.
Watson, RA, 2000, “Analysis of Recombinative Algorithms on a Hierarchical Building-Block Problem”, FOGA 6, Fogarty, Martin, Spears, eds. Springer, to appear (2001).
Watson, RA, & Pollack, JB, 2000a, “Combination and Recombination in Genetic Algorithms”, technical report CS-00-209, Dept. Computer Science, Brandeis University.
Watson, R.A. & Pollack, J.B. 2000b, “Recombination Without Respect: Schema Combination and Disruption in Genetic Algorithm Crossover”, Procs. of GECCO 2000, Whitley, D, et al (eds.), Morgan Kaufmann, 2000.
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Watson, R.A., Pollack, J.B. (2000). Symbiotic Combination as an Alternative to Sexual Recombination in Genetic Algorithms. In: , et al. Parallel Problem Solving from Nature PPSN VI. PPSN 2000. Lecture Notes in Computer Science, vol 1917. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45356-3_42
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DOI: https://doi.org/10.1007/3-540-45356-3_42
Publisher Name: Springer, Berlin, Heidelberg
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