Abstract
Local selection (LS) is a very simple selection scheme in evolutionary algorithms. Individual fitnesses are compared to a fixed threshold, rather than to each other, to decide who gets to reproduce. LS, coupled with fitness functions stemming from the consumption of shared environmental resources, maintains diversity in a way similar to fitness sharing; however it is generally more efficient than fitness sharing, and lends itself to parallel implementations for distributed tasks. While LS is not prone to premature convergence, it applies minimal selection pressure upon the population. LS is therefore more appropriate than other, stronger selection schemes only on certain problem classes. This papers characterizes one broad class of problems in which LS consistently out-performs tournament selection.
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References
Y. Davidor. A naturally occurring niche and species phenomenon: The model and first results. In R. Belew and L. Booker, editors, 4th ICGA, 1991.
K. De Jong, An analysis of the behavior of a class of genetic adaptive systems, PhD thesis, University of Michigan, 1975.
K. De Jong and J. Sarma. On decentralizing selection algorithms. In 6th ICGA, 1995.
M. Dorigo and L. Gambardella. Ant colony system: A cooperative learning approach to the traveling salesman problem. IEEE Trans. on Evolutionary Computation, 1(1):53–66, 1997.
L. Eshelman and J. Schaffer. Crossover's niche. In 5th ICGA, 1993.
D. Goldberg. A note on Boltzmann tournament selection for genetic algorithms and population-oriented simulated annealing. Complex Sustems, 4:445–460, 1990.
D. Goldberg and J. Richardson. Genetic algorithms with sharing for multimodal function optimization. In 2nd ICGA, 1987.
G. Harik. Finding multimodal solutions using restricted tournament selection. In 6th ICGA, 1995.
D. Hartl and A. Clarke. Principles of Population Genetics. Sinauer Associates, 1989.
D. Johnson. Local optimization and the traveling salesman problem. In 17th Colloquium on Automata, Languages and Programming, 1990.
S. Mahfoud. Population sizing for sharing methods. In FOGA 3, 1994.
S. Mahfoud. A comparison of parallel and sequential niching methods. In 6th ICGA, 1995.
F. Menczer. Arachnid: Adaptive Retrieval Agents Choosing Heuristic Neighborhoods for Information Discovery. In 14th ICML, 1997.
P. Menczer and R. Belew. From complex environments to complex behaviors. Adaptive Behavior, 4:317–363, 1996.
F. Menczer and R. Belew. Latent energy environments. In Adaptive Individuals in Evolving Populations: Models and Algorithms. Addison Wesley, 1996.
F. Menczer and D. Parisi. Recombination and unsuperviscd learning: effects of crossover in the genetic optimization of neural networks. Network, 3:423–442, 1992.
C. Papadimitriou. On selecting a satisfying truth assignment. In 32nd FOCS, 1991.
B. Selman, D. Mitchell, and H. Levesque. Generating hard satisfiability problems. Artificial Intelligence, 81:17–29, 1996.
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Menczer, F., Belew, R.K. (1998). Local selection. In: Porto, V.W., Saravanan, N., Waagen, D., Eiben, A.E. (eds) Evolutionary Programming VII. EP 1998. Lecture Notes in Computer Science, vol 1447. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0040821
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DOI: https://doi.org/10.1007/BFb0040821
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