- author = "Stuart W. Card and Chilukuri K. Mohan",
- title = "An Application of Information Theoretic Selection to Evolution of Models with Continuous-valued Inputs",
- booktitle = "Genetic Programming Theory and Practice {VI}",
- year = "2008",
- editor = "Rick L. Riolo and Terence Soule and Bill Worzel",
- series = "Genetic and Evolutionary Computation",
- chapter = "3",
- pages = "29--43",
- address = "Ann Arbor",
- month = "15-17 " # may,
- publisher = "Springer",
- keywords = "genetic algorithms, genetic programming, genetic programming, information theory, input selection, building blocks, ensemble models, diversity, fitness, entropy, mutual information, redundancy, synergy, similarity, information distance, evolvability, heritability, sufficiency, necessity, copula",
- isbn13 = "978-0-387-87622-1",
-
DOI = "
doi:10.1007/978-0-387-87623-8_3",
- size = "14 pages",
-
abstract = "Information theoretic functionals have significant
benefits as compared with traditional error based
indicators of fitness and diversity. Mutual Information
(MI), various normalizations of it, and similarity and
distance metrics derived from it, can be used
advantageously in all phases of Genetic Programming
(GP), starting with input selection. However, these
functionals are based on Shannon’s entropy, which is
strictly defined only for discrete random variables, so
their application to problems involving continuous
valued data requires their generalization and
development of robust and efficient algorithms for
their calculation. This paper outlines such algorithms
and illustrates their application to a noisy continuous
valued data set synthesized to test GP symbolic
regression systems (Korns, 2007).
Information theoretic sufficiency outperforms linear correlation in ranking the relevance of available inputs in this data set. Similar results are obtained on inputs filtered by functions that ‘fold’ the data, thereby destroying information; ranking these intermediate evolutionary forms, sufficiency again outperforms correlation. Sufficiency also exhibits a distinct threshold separating irrelevant terms from terms that are indeed relevant in regression of these test problems. As a less computationally costly alternative to rankings of entire populations, tournament selection is often used; on this data set, for pairwise tournament selection, sufficiency greatly outperforms correlation. Multi-objective ranking, considering also information theoretic necessity to prefer appropriately filtered inputs (over corresponding raw inputs with excess entropies), is foreshadowed.",
- notes = "part of \cite{Riolo:2008:GPTP} published in 2009",
