Automatically designing selection heuristics

Abstract

In a standard evolutionary algorithm such as genetic algorithms (GAs), a selection mechanism is used to decide which individuals are to be chosen for subsequent mutation. Examples of selection mechanisms are fitness-proportional selection, in which individuals are chosen with a probability directly in proportion to their fitness value, and rank selection, in which individuals are selected with a probability in proportion to their ordinal ranking by fitness. These two human-designed selection heuristics implicitly assume that fitter individuals produce fitter offspring. Whilst one might invest human ingenuity in the construction of alternative selection heuristics, the approach adopted in this paper is to represent a generic family of selection heuristics which are applied via an algorithmic framework. We then generate instances of selection heuristics and test their performance in an evolutionary algorithm (which in this paper tackles a variety of bitstring optimization problems). The representation we use for the program space is a register machine (a set of real-valued registers on which a program is executed). Fitness-proportional and rank selection can be expressed as one-line programs, and more sophisticated selection heuristics may also be expressed. The result is a system which produces selection heuristics that outperform either of the original selection heuristics.