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Adaptive evolution: an efficient heuristic for global optimization

Published: 08 July 2009 Publication History

Abstract

This paper presents a novel evolutionary approach to solve numerical optimization problems, called Adaptive Evolution (AEv). AEv is a new micro-population-like technique because it uses small populations (less than 10 individuals). The two main mechanisms of AEv are elitism and adaptive behavior. It has an adaptive parameter to adjust the balance between global exploration, local exploitation and elitism. Its two crossover operators allow a newly-generated offspring to be parent of other offspring in the same generation. AEv requires the fine-tuning of two parameters (several state-of-the-art approaches use at least three). AEv is tested on a set of 10 benchmark functions with 30 decision variables and it is compared with respect to some state-of-the-art algorithms to show its competitive performance.

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Cited By

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  • (2021)Controller Tuning by Metaheuristics OptimizationController Tuning Optimization Methods for Multi-Constraints and Nonlinear Systems10.1007/978-3-030-64541-0_2(11-51)Online publication date: 7-Jan-2021
  • (2018)Metaheuristic research: a comprehensive surveyArtificial Intelligence Review10.1007/s10462-017-9605-zOnline publication date: 13-Jan-2018

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Published In

cover image ACM Conferences
GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation
July 2009
2036 pages
ISBN:9781605583259
DOI:10.1145/1569901

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 July 2009

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Author Tags

  1. evolutionary algorithms
  2. meta-heuristics
  3. numerical optimization

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GECCO09
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GECCO09: Genetic and Evolutionary Computation Conference
July 8 - 12, 2009
Québec, Montreal, Canada

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

View all
  • (2021)Controller Tuning by Metaheuristics OptimizationController Tuning Optimization Methods for Multi-Constraints and Nonlinear Systems10.1007/978-3-030-64541-0_2(11-51)Online publication date: 7-Jan-2021
  • (2018)Metaheuristic research: a comprehensive surveyArtificial Intelligence Review10.1007/s10462-017-9605-zOnline publication date: 13-Jan-2018

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