Raising theoretical questions about the utility of genetic algorithms

Abstract

Genetic algorithms are believed by some to be very efficient optimization and adaptation tools. So far, the efficacy of genetic algorithms has been described by empirical results, and yet theoretical approaches are far behind. This paper aims at raising fundamental theoretical questions about the utility of genetic algorithms. These questions originate from various existing theories and the no-free-lunch theorem, a theory that compares all possible optimization procedure with respect to an equal distribution of all possible objective functions. While these questions are open at least in part, they all indicate that genetic algorithms yield worse performance than any other (deterministic) optimization algorithm. Consequently, future research should answer the question of whether the real world (or another application domain) imposes a non-equal distribution for which genetic algorithms yield advantageous performance, or whether genetic algorithms should apply operators in a deterministic fashion.