Abstract
Evolution Strategies (ESs)[15] and Genetic Algorithms (GAs)[13] have both been used to optimise functions, using the natural process of evolution as inspiration for their search mechanisms. The ES uses gene mutation as it's main search operator whilst the GA mainly relies upon gene recombination. This paper describes how the addition of a second mutation operator, used in conjunction with the mutation and crossover operators of the normal GA, can improve the GA's performance on rugged fitness landscapes. We then show that by adding Lamarckian replacement the GA's performance on smooth landscapes can also be improved, further improving it's performance on rugged landscapes. We explain how the extra operators allow the GA to gain and exploit local information about the fitness landscape, and how this local random hill climbing can be seen to combine the search characteristics of the ES with those of the GA.
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References
Ackley D. H. & Littman M. (1991), “Interactions Between Learning and Evolution”, Artificial Life II, Addison-Wesley.
BÄck T. (1992), “Self-Adaption in Genetic Algorithms”, Toward A Practice of Autonomous Systems, MIT Press.
Baldwin J. M. (1896), “A New Factor in Evolution”, American Naturalist, 30.
Belew R. K., McInerney J. & Schraudolph N. N. (1991), “Evolving Networks: Using the Genetic Algorithm with Connectionist Learning”, Artificial Life II, Addison-Wesley.
Braun H. (1993), “Evolution — a Paradigm for Constructing Intelligent Agents”, From Animals to Animats II, MIT Press.
Davis L. (1991), “Bit-Climbing, Representational Bias, and Test Suite Design”, International Conference on Genetic Algorithms IV, Morgan Kauffman.
Fogel L. J., Owens A. J. & Walsh M. J. (1962), “Artificial Intelligence Through Simulated Evolution”, John Wiley, N.Y.
Forrest S., Smith R. E., Perelson A. S. & Javornik B. (1993), “Using Genetic Algorithms to Explore Pattern Recognition in the Immune System”, Evolutionary Computation, 1, 3.
Goldberg D. E. (1989), “Genetic Algorithms and Walsh Functions: Part II, deception and its analysis”, Complex Systems, 3.
Gruau F. & Whitley D. (1993), “Adding Learning to the Cellular Development of Neural Networks”, Evolutionary Computation, 1, 3.
Hinton G. E. & Nowlan S. J. (1987), “How Learning Can Guide Evolution”, Complex Systems 1.
Hoffmeister F. & BÄck T. (1990), “Genetic Algorithms and Evolution Strategies: Similarities and Differences”, Parallel Problem Solving from Nature, Univ. of Dortmund.
Holland J. H. (1975), “Adaption in Natural and Artificial Systems”, Univ. of Michigan Press, Ann Arbor.
Mitchell M., Forrest S, & Holland J. H. (1992), “The Royal Road for Genetic Algorithms: Fitness Landscapes and GA Performance”, Toward A Practice of Autonomous Systems, MIT Press.
Mülenbein H. (1991), “Evolution in Time and Space — the Parallel Genetic Algorithm”, Foundations of Genetic Algorithms, Morgan Kaufman.
Rechenberg I. (1973), “Evolutionsstrategie: Optimierung Technischer Systeme Nach Prinzipien der Biologischen Evolution”, Frommann-Holzboog Verlag, Stuttgart.
Schwefel H-P. (1977), “Numerische Optimierung von Computer-Modellen Mittels der Evolutionsstrategie”, Interdisciplinary Systems Research; 26.BirkhÄuser, Basel.
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© 1994 Springer-Verlag Berlin Heidelberg
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Bull, L., Fogarty, T.C. (1994). An evolution strategy and genetic algorithm hybrid: An initial implementation and first results. In: Fogarty, T.C. (eds) Evolutionary Computing. AISB EC 1994. Lecture Notes in Computer Science, vol 865. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-58483-8_8
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DOI: https://doi.org/10.1007/3-540-58483-8_8
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