Ensemble selection for evolutionary learning using information theory and price's theorem

This paper presents an information theoretic perspective on design and analysis of evolutionary algorithms. Indicators of solution quality are developed and applied not only to individuals but also to ensembles, thereby ensuring information diversity. Price's Theorem is extended to show how joint indicators can drive reproductive sampling rate of potential parental pairings. Heritability of mutual information is identified as a key issue. Categories and Subject Descriptors I.2.6 [Artificial Intelligence]: Learning

• Type: Conference
• Year: 2006
• Where: GECCO
• Authors: Stuart W. Card, Chilukuri K. Mohan
• Keywords: GECCO 2006, Information Theoretic Perspective, Optimization, Potential Parental Pairings, Reproductive Sampling Rate