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Climbing Unimodal Landscapes With Neutrality: A Case Study Of The One-max Problem

We investigate fitness neutrality in a Simple Evolutionary Algorithm (SEA) and in a neutrality-enabled evolutionary system using the OneMax problem. The results show that with the support of limited neutrality, SEA is less effective than our system where a larger amount of neutrality is supported. In order to understand the role of neutrality in evolutionary search of this unimodal landscape, we have created a theoretical framework that gives the number of gene changes under different levels of neutrality. The interim results of this theoretical work are also presented.