A comparative study on self-adaptive differential evolution algorithms for test functions and a real-world problem

Shota Eguchi, Yuki Matsugano, Hirokazu Sakaguchi, Satoshi Ono, Hisato Fukuda, Ryo Furukawa, Hiroshi Kawasaki

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

Abstract

This paper compares novel self-adaptive Differential Evolution algorithms (SADEs) on noisy test functions to see how robust the algorithms are against noise in fitness function. This paper also compares the performance of SADEs on real-world problems that estimates Bidirectional Reflectance Distribution Function properties of 3D objects.

Original languageEnglish
Title of host publicationLearning and Intelligent Optimization - 9th International Conference, LION, Revised Selected Papers
EditorsClarisse Dhaenens, Laetitia Jourdan, Marie-Eleonore Marmion
PublisherSpringer Verlag
Pages131-136
Number of pages6
ISBN (Print)9783319190839
DOIs
Publication statusPublished - Jan 1 2015
Externally publishedYes
Event9th International Conference on Learning and Intelligent Optimization, LION 2015 - Lille, France
Duration: Jan 12 2015Jan 15 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8994
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other9th International Conference on Learning and Intelligent Optimization, LION 2015
CountryFrance
CityLille
Period1/12/151/15/15

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Eguchi, S., Matsugano, Y., Sakaguchi, H., Ono, S., Fukuda, H., Furukawa, R., & Kawasaki, H. (2015). A comparative study on self-adaptive differential evolution algorithms for test functions and a real-world problem. In C. Dhaenens, L. Jourdan, & M-E. Marmion (Eds.), Learning and Intelligent Optimization - 9th International Conference, LION, Revised Selected Papers (pp. 131-136). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8994). Springer Verlag. https://doi.org/10.1007/978-3-319-19084-6_12