Abstract
Setting the mutation rate for an evolutionary algorithm (EA) is confounded by many issues. Here we investigate mutation rates mainly in the context of large-population-parallelism. We justify the notion that high rates achieve better results, using underlying theory which notices that parallelization favourably alters the fitness distribution of a mutation operator. We derive an expression which sets out how this is changed in terms of the level of parallelization, and derive further expressions that allow us to adapt the mutation rate in a principled way by exploiting online-sampled landscape information. The adaptation technique (called RAGE– Rate Adaptation with Gain Expectation) shows promising preliminary results. Our motivation is the field of Directed Evolution (DE), which uses large-scale parallel EAs for limited numbers of generations to evolve novel proteins. RAGE is highly suitable for DE, and is applicable to large-scale parallel EAs in general.
Present address: Dept Chemistry, UMIST, PO Box 88, MANCHESTER M60 1QD
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References
Arnold, F. M. (ed). Evolutionary protein design. Advances in Protein Chemistry, vol. 55. Academic Press, San Diego, 2001.
Arnold F. Combinatorial and computational challenges for biocatalyst design. Nature 2001;409:253–7.
Bäck T, Optimal Mutation Rates in Genetic Search, Proc. 5th ICGA, pp 2–9, 1993.
Bäck T, Evolutionary Algorithms in Theory and Practice, OUP, 1996.
Baltz, RH. Mutation in Streptomyces. In: Day L, Queener S, editors. The Bacteria, Vol 9, Antibiotic-producing Streptomyces. Academic Press, 1986:61–94.
Blickle, T., Thiele, L. (1995). A Mathematical Analysis of Tournament Selection, in L.J. Eshelman (ed.) Proc. 6th International Conference on Genetic Algorithms, Morgan Kaufmann, pp. 9–16.
Cantú-Paz, E. (2000). Efficient and Accurate Parallel Genetic Algorithms, Kluwer Academic Publishers.
Fogel, D.B. and Ghozeil, A. (1996). Using Fitness Distributions to Design More Efficient Evolutionary Computations, in Proceedings of the 3rd International Conference on Evolutionary Computation, IEEE, pp. 11–19.
Mühlenbein, H. How genetic algorithms really work: I. Mutation and Hillclimbing, in R. Manner, B. Manderick (eds), Proc. 2nd Int’l Conf. on Parallel Problem Solving from Nature, Elsevier, pp 15–25.
Oates, M. and Corne, D. Overcoming Fitness Barriers in Multi-Modal Search Spaces, in Foundations of Genetic Algorithms 6 (2000), Morgan Kaufmann.
Rechenberg I, Evolutionsstrategie: Optimierung technischer Systeme nach Prinzipien der biologischen Evolution, Frommann-Holzboog, Stuttgart, 1973
Voigt CA, Kauffman S & Wang ZG. Rational evolutionary design: The theory of in vitro protein evolution. In: Arnold FM, editor. Advances in Protein Chemistry, Vol 55, 2001:79–160.
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Corne, D.W., Oates, M.J., Kell, D.B. (2002). On Fitness Distributions and Expected Fitness Gain of Mutation Rates in Parallel Evolutionary Algorithms. In: Guervós, J.J.M., Adamidis, P., Beyer, HG., Schwefel, HP., Fernández-Villacañas, JL. (eds) Parallel Problem Solving from Nature — PPSN VII. PPSN 2002. Lecture Notes in Computer Science, vol 2439. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45712-7_13
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DOI: https://doi.org/10.1007/3-540-45712-7_13
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