Binary versus real coding for genetic algorithms: A false dichotomy?

Abstract

The usefulness of the genetic algorithm (GA) as judged by numerous applications in engineering and other contexts cannot be questioned. However, to make the application successful, often considerable effort is needed to customise the GA to suit the problem or class of problems under consideration. Perhaps the most basic decision which the designer of a GA makes, is whether to use binary or real coding. If the variable of the parameter space of an optimisation problem is continuous, a real coded GA is possibly indicated. Real numbers have a floating-point representation on a computer and the decision space is always discretised; it is not immediately evident that real coding should be the preferred method for encoding this particular problem. We re-visit this, and other decisions, which GA designers need to make. We present simulations on a standard test function, which show the result that no one GA performs best on every test problem. Perhaps the initial choice to code a problem using a real or binary coding is a false dichotomy. What counts are the algorithms for implementing the genetic operators and these algorithms are a consequence of the coding.