Practical evaluation of efficient fitness functions for binary images

Abstract

Genetic Programming can be used to evolve complex objects. One field, where GP may be used is image analysis. There are several works using evolutionary methods to process, analyze or classify images. All these procedures need an appropriate fitness function, that is a similarity measure. However, computing such measures usually needs a lot of computational time. To solve this problem, the notion of efficiently computable fitness functions was introduced, and their theory was already examined in detail. In contrast to that work, in this paper the practical aspects of these fitness functions are discussed. © Springer-Verlag Berlin Heidelberg 2005.