Evolving Ensembles: What Can We Learn from Biological Mutualisms?

Michael Adam Lones, Stuart E. Lacy, Stephen L. Smith

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)


Ensembles are groups of classifiers which cooperate in order to reach a decision. Conventionally, the members of an ensemble are trained sequentially, and typically independently, and are not brought together until the final stages of ensemble generation. In this paper, we discuss the potential benefits of training classifiers together, so that they learn to interact at an early stage of their development. As a potential mechanism for achieving this, we consider the biological concept of mutualism, whereby cooperation emerges over the course of biological evolution. We also discuss potential mechanisms for implementing this approach within an evolutionary algorithm context.
Original languageEnglish
Title of host publicationInformation Processing in Cells and Tissues
Subtitle of host publication10th International Conference, IPCAT 2015, San Diego, CA, USA, September 14-16, 2015, Proceedings
EditorsMichael Lones, Andy Tyrrell, Stephen Smith, Gary Fogel
Number of pages9
ISBN (Electronic)978-3-319-23108-2
ISBN (Print)978-3-319-23107-5
Publication statusPublished - 2 Sept 2015
Event10th International Conference - California, San Diego, United States
Duration: 14 Sept 201516 Sept 2015

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743


Conference10th International Conference
Abbreviated titleIPCAT 2015
Country/TerritoryUnited States
CitySan Diego
OtherInformation Processing in Cells and Tissues

ASJC Scopus subject areas

  • General Computer Science
  • Theoretical Computer Science


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