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The baldwin effect on a memetic differential evolution for constrained numerical optimization problems

Published: 15 July 2017 Publication History

Abstract

This paper analyzes the Baldwin effect on a memetic algorithm that solves constrained numerical optimization problems (CNOPS). For this study the canonical Differential Evolution (DE) enhanced with the Hooke-Jeeves method (HJ) as local search operator is proposed (MDEHJ), which implements a probabilistic scheme to activate HJ by means a sinusoidal function that considers the population diversity. Three MDEHJ instances are applied to study the Baldwin effect in different exploitation areas (best, worst and random selected, respectively). Final results are compared against those obtained by MDEHJ with Lamarckian learning. All instances are tested on thirty-six well-known benchmark problems. The results suggest that the proposed approach is suitable to solve CNOPS and those results also show that Baldwin effect does not affect the performance of a memetic DE in constrained search spaces.

References

[1]
Andrea Caponio, Ferrante Neri, and Ville Tirronen. 2009. Super-fit control adaptation in memetic differential evolution frameworks. Soft Computing 13, 8--9 (2009), 811--831.
[2]
Robert Hooke and T. A. Jeeves. 1961. "Direct Search" Solution of Numerical and Statistical Problems. J. ACM 8, 2 (1961), 212--229.
[3]
R. Mallipeddi and P.N. Suganthan. 2010. Problem Definitions and Evaluation Criteria for the CEC 2010 Competition on Constrained Real-Parameter Optimization. Technical Report. Nanyang Technological University, Singapore.
[4]
Rainer Storn and K Price. 1997. Differential evolution a simple and efficient heuristic for global optimization over continuous spaces. Journal of global optimization (1997), 341--359.
[5]
Tetsuyuki Takahama, Setsuko Sakai, and Noriyuki Iwane. 2005. Constrained Optimization by the Constrained Hybrid Algorithm of Particle Swarm Optimization and Genetic Algorithm. In AI 2005: Advances in Artificial Intelligence, Shichao Zhang and Ray Jarvis (Eds.). Lecture Notes in Computer Science, Vol. 3809. Springer Berlin Heidelberg, 389--400.

Cited By

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  • (2020)Development and validation of constraints handling in a Differential Evolution optimizerINCAS BULLETIN10.13111/2066-8201.2020.12.1.612:1(59-66)Online publication date: 1-Mar-2020
  • (2019)A New Hybrid Metaheuristic for Equality Constrained Bi-objective Optimization ProblemsEvolutionary Multi-Criterion Optimization10.1007/978-3-030-12598-1_5(53-65)Online publication date: 3-Feb-2019
  • (2019)Memetic Algorithms for Business Analytics and Data Science: A Brief SurveyBusiness and Consumer Analytics: New Ideas10.1007/978-3-030-06222-4_13(545-608)Online publication date: 31-May-2019

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cover image ACM Conferences
GECCO '17: Proceedings of the Genetic and Evolutionary Computation Conference Companion
July 2017
1934 pages
ISBN:9781450349390
DOI:10.1145/3067695
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 15 July 2017

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Author Tags

  1. baldwin effect
  2. constrained numerical optimization
  3. differential evolution
  4. memetic algorithm

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

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Cited By

View all
  • (2020)Development and validation of constraints handling in a Differential Evolution optimizerINCAS BULLETIN10.13111/2066-8201.2020.12.1.612:1(59-66)Online publication date: 1-Mar-2020
  • (2019)A New Hybrid Metaheuristic for Equality Constrained Bi-objective Optimization ProblemsEvolutionary Multi-Criterion Optimization10.1007/978-3-030-12598-1_5(53-65)Online publication date: 3-Feb-2019
  • (2019)Memetic Algorithms for Business Analytics and Data Science: A Brief SurveyBusiness and Consumer Analytics: New Ideas10.1007/978-3-030-06222-4_13(545-608)Online publication date: 31-May-2019

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