Fitness uniform optimization

Marcus Hutter*, Shane Legg

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

59 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)568-589
Number of pages22
JournalIEEE Transactions on Evolutionary Computation
Issue number5
Publication statusPublished - Oct 2006
Externally publishedYes


Dive into the research topics of 'Fitness uniform optimization'. Together they form a unique fingerprint.

Cite this