Competitive Environments Evolve Better Solutions for Complex Tasks

Abstract

In the typical genetic algorithm experiment, the fitness function is constructed to be independent of the contents of the population to provide a consistent objective measure. Such objectivity entails significant knowledge about the environment which suggests either the problem has previously been solved or other non-evolutionary techniques may be more efficient. Furthermore, for many complex tasks an independent fitness function is either impractical or impossible to provide. In this paper, we demonstrate that competitive fitness functions, i.e. fitness functions that are dependent on the constituents of the population, can provide a more robust training environment than independent fitness functions. We describe three differing methods for competitive fitness, and discuss their respective advantages. 1 INTRODUCTION Competitive learning is a long standing topic in machine learning (Samuel, 1959; Tesauro, 1992). Interest for using competition in machine learning tasks stems from a de...