Local information of fitness landscape obtained by paired comparison-based memetic search for interactive differential evolution

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

3 Citations (Scopus)

Abstract

We propose a triple comparison-based interactive differential evolution (IDE) algorithm. The comparison of target vector and trail vector supports a local fitness landscape for IDE algorithm to conduct a memetic search. Besides target vector and trail vector in canonical IDE algorithm framework, we conduct a memetic search around whichever is the vector with better fitness. We use a random number from a normal distribution generator or a uniform distribution generator to perturb the vector for generating a third vector. By comparing the target vector, the trail vector and the third vector, we implement a triple comparison mechanism in IDE algorithm. A Gaussian mixture model is applied as a pseudo IDE user in our evaluation. We compare our proposal with canonical IDE and triple comparisonbased IDE implemented by opposite-based learning, and apply several statistical tests to investigate the significance of our proposed algorithm. From the evaluation results, our proposed triple comparison-based IDE algorithm shows significantly better performance optimization. We also investigate potential issues arising from our proposal, and discuss some open topics and future opportunities.

Original languageEnglish
Title of host publication2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2215-2221
Number of pages7
ISBN (Electronic)9781479974924
DOIs
Publication statusPublished - Sep 10 2015
EventIEEE Congress on Evolutionary Computation, CEC 2015 - Sendai, Japan
Duration: May 25 2015May 28 2015

Publication series

Name2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings

Other

OtherIEEE Congress on Evolutionary Computation, CEC 2015
CountryJapan
CitySendai
Period5/25/155/28/15

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Computational Mathematics

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