Sustaining diversity using behavioral information distance

Conventional similarity metrics used to sustain diversity in evolving populations are not well suited to sequential decision tasks. Genotypes and phenotypic structure are poor predictors of how solutions will actually behave in the environment. In this paper, we propose measuring similarity directly on the behavioral trajectories of evolving candidate policies using a universal similarity measure based on algorithmic information theory: normalized compression distance (NCD). NCD is compared to four other similarity measures in both genotype and phenotype space on the POMDP Tartarus problem, and shown to produce the most fit, general, and complex solutions. Categories and Subject Descriptors I.2.8 [Artificial Intelligence]: Problem Solving, Control Methods, and Search—heuristic methods General Terms Algorithms

• Type: Conference
• Year: 2009
• Where: GECCO
• Authors: Faustino J. Gomez
• Keywords: Artificial Intelligence, Conventional Similarity Metrics, GECCO 2009, Similarity Measures, Universal Similarity Measure