Path-extrapolation in Evolutionary Algorithms

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Evolutionary Algorithms are used to solve challenging optimization problems across a variety of domains. While simple and robust they often do not effectively exploit information generated during the search which in turn degrades their efficiency and often results in a slow convergence. As such this paper presents a new algorithm to accelerate the EA convergence by monitoring the path traversed by its population over several generations. This information is then used to construct interpolating polynomials which predict the position of the centroid in the next generation and is then used to shift the population along that direction to accelerate convergence. An extensive numerical performance analysis shows the effectiveness of the proposed approach.

Original languageEnglish
Title of host publicationConference Proceedings - AIMLR 2023
Subtitle of host publication2023 Asia Conference on Artificial Intelligence, Machine Learning and Robotics
ISBN (Electronic)9798400708312
DOIs
StatePublished - 15 Sep 2023
Event2023 Asia Conference on Artificial Intelligence, Machine Learning and Robotics, AIMLR 2023 - Bangkok, Thailand
Duration: 15 Sep 202317 Sep 2023

Publication series

NameACM International Conference Proceeding Series

Conference

Conference2023 Asia Conference on Artificial Intelligence, Machine Learning and Robotics, AIMLR 2023
Country/TerritoryThailand
CityBangkok
Period15/09/2317/09/23

Keywords

  • Convergence
  • Evolutionary Algorithms
  • Optimization

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