Fitness Sharing Based on Angular Distances

It is commonly believed that diversity is crucial for an evolutionary system to succeed, especially when the problem to be solved contains local optima from which the population cannot easily escape. There exist numerous methods to maintain the diversity of an evolving population, but it is not always clear what kind of diversity is helpful in a given situation. In this paper we show that striving to maintain high angular distances between the fitness vectors of the individuals in a population leads to better results in most cases considered. Without increased computational costs, our angular sharing scheme enables the evolutionary system in most cases to find better solutions than other sharing schemes investigated.