Fitness Uniform Optimization

TL;DR

This paper introduces a novel fitness uniform selection scheme in evolutionary algorithms that promotes diversity by selecting individuals uniformly across fitness levels, improving optimization on complex problems.

Contribution

It proposes a new selection method based on uniform fitness distribution and a deletion scheme to better preserve diversity, outperforming standard methods.

Findings

Enhanced performance on deceptive and NP-hard problems

Better preservation of genetic diversity

More effective than tournament selection and random deletion

Abstract

In evolutionary algorithms, the fitness of a population increases with time by mutating and recombining individuals and by a biased selection of more fit 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.