Fitness Importance for online evolution

Philip Valencia*, Raja Jurdak, Peter Lindsay

*Corresponding author for this work

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

Abstract

To complement standard fitness functions, we propose "Fitness Importance" (FI) as a novel meta-heuristic for online learning systems. We define FI and show how it can be used to dynamically bias the population composition in order to vary the instantaneous system performance at a tradeoff to learning capability. The effect of FI is demonstrated on a simple light-sensing and light-actuating optimisation problem running on multiple wireless sensor network devices. We also describe how FI can be used with the In situ Distributed Genetic Programming (IDGP) framework to balance learning and performing for resource-constrained computing devices which evolve their logic continuously.

Original languageEnglish
Title of host publicationProceedings of the 12th Annual Genetic and Evolutionary Computation Conference, GECCO '10 - Companion Publication
Place of PublicationNew York
PublisherACM
Pages2117-2118
Number of pages2
ISBN (Print)9781450300735
DOIs
Publication statusPublished - 2010
Externally publishedYes
Event12th Annual Genetic and Evolutionary Computation Conference, GECCO-2010 - Portland, OR, United States
Duration: 7 Jul 201011 Jul 2010

Other

Other12th Annual Genetic and Evolutionary Computation Conference, GECCO-2010
CountryUnited States
CityPortland, OR
Period7/07/1011/07/10

Keywords

  • Adaptive
  • Evolution
  • Fitness
  • Genetic Program
  • Late breaking abstract
  • Objective
  • Online
  • Wireless sensor network

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