Critical factors in the performance of novelty search

Novelty search is a recently proposed method for evolutionary computation designed to avoid the problem of deception, in which the fitness function guides the search process away from global optima. Novelty search replaces fitnessbased selection with novelty-based selection, where novelty is measured by comparing an individual’s behavior to that of the current population and an archive of past novel individuals. Though there is substantial evidence that novelty search can overcome the problem of deception, the critical factors in its performance remain poorly understood. This paper helps to bridge this gap by analyzing how the behavior function, which maps each genotype to a behavior, affects performance. We propose the notion of descendant fitness probability (DFP), which describes how likely a genotype’s descendants are to have a certain fitness, and formulate two hypotheses about when changes to the behavior function will improve novelty search’s performance, based on th...

• Type: Journal
• Year: 2011
• Where: GECCO
• Authors: Steijn Kistemaker, Shimon Whiteson
• Keywords: Behavior Function, Evolutionary Computation, GECCO 2011, Measurement Performance, Optimization