Fitness uniform optimisation
Date
2006
Authors
Hutter, Marcus
Legg, Shane
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Institute of Electrical and Electronics Engineers (IEEE Inc)
Abstract
In evolutionary algorithms, the fitness of a population increases with time by mutating and recombining individuals and by a biased selection of fitter individuals. The right selection pressure is critical in ensuring sufficient optimization progress on the one hand and in preserving genetic diversity to be able to escape from local optima on the other hand. Motivated by a universal similarity relation on the individuals, we propose a new selection scheme, which is uniform in the fitness values. It generates selection pressure toward sparsely populated fitness regions, not necessarily toward higher fitness, as is the case for all other selection schemes. We show analytically on a simple example that the new selection scheme can be much more effective than standard selection schemes. We also propose a new deletion scheme which achieves a similar result via deletion and show how such a scheme preserves genetic diversity more effectively than standard approaches. We compare the performance of the new schemes to tournament selection and random deletion on an artificial deceptive problem and a range of NP hard problems: traveling salesman, set covering, and satisfiability.
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Keywords: Fitness tree model; Fitness uniform deletion scheme; Fitness uniform selection scheme; Local optima; Set covering; Computational complexity; Evolutionary algorithms; Operations research; Set theory; Trees (mathematics); Optimization Fitness tree model; Fitness uniform deletion scheme; Fitness uniform selection scheme; Local optima; Preserve diversity; Satisfiability; Set covering; Traveling salesman
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Source
IEEE Transactions on Evolutionary Computation
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Journal article
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2037-12-31
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