Multiobjective information theoretic ensemble selection

Stuart W. Card, Chilukuri K. Mohan

Research output: Chapter in Book/Entry/PoemConference contribution

1 Scopus citations


In evolutionary learning, the sine qua non is evolvability, which requires heritability of fitness and a balance between exploitation and exploration. Unfortunately, commonly used fitness measures, such as root mean squared error (RMSE), often fail to reward individuals whose presence in the population is needed to explain important data variance; and indicators of diversity generally are not only incommensurate with those of fitness but also essentially arbitrary. Thus, due to poor scaling, deception, etc., apparently relatively high fitness individuals in early generations may not contain the building blocks needed to evolve optimal solutions in later generations. To reward individuals for their potential incremental contributions to the solution of the overall problem, heritable information theoretic functionals are developed that incorporate diversity considerations into fitness, explicitly identifying building blocks suitable for recombination (e.g. for non-random mating). Algorithms for estimating these functionals from either discrete or continuous data are illustrated by application to input selection in a high dimensional industrial process control data set. Multiobjective information theoretic ensemble selection is shown to avoid some known feature selection pitfalls.

Original languageEnglish (US)
Title of host publicationEvolutionary and Bio-Inspired Computation
Subtitle of host publicationTheory and Applications III
StatePublished - 2009
EventEvolutionary and Bio-Inspired Computation: Theory and Applications III - Orlando, FL, United States
Duration: Apr 14 2009Apr 15 2009

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X


OtherEvolutionary and Bio-Inspired Computation: Theory and Applications III
Country/TerritoryUnited States
CityOrlando, FL


  • Copula
  • Ensemble models
  • Information dimension
  • Input selection
  • Mutual information

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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