Accelerating evolutionary computation using estimated convergence points

Jun Yu; Yan Pei; Hideyuki Takagi

doi:10.1109/CEC.2016.7743959

Accelerating evolutionary computation using estimated convergence points

Jun Yu, Yan Pei, Hideyuki Takagi

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

12 Citations (Scopus)

Abstract

We use the convergence points estimated by our proposed method as elite individuals for evolutionary computation and evaluate the acceleration effect and analyze the effect and computational cost. The worst individuals in population are replaced with the convergence points estimated from the moving vectors between parent individuals and their offspring; i.e. these convergence points are used as elite individuals. Differential evolution (DE) and 14 benchmark functions are used in our evaluation experiments. The experimental results show that use of the estimated convergence points as elite can accelerate DE search in spite of the calculation cost of the convergence points. We finally analyze the components of the proposed estimation method to improve cost-performance.

Original language	English
Title of host publication	2016 IEEE Congress on Evolutionary Computation, CEC 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1438-1444
Number of pages	7
ISBN (Electronic)	9781509006229
DOIs	https://doi.org/10.1109/CEC.2016.7743959
Publication status	Published - Nov 14 2016
Event	2016 IEEE Congress on Evolutionary Computation, CEC 2016 - Vancouver, Canada Duration: Jul 24 2016 → Jul 29 2016

Publication series

Name	2016 IEEE Congress on Evolutionary Computation, CEC 2016

Other

Other	2016 IEEE Congress on Evolutionary Computation, CEC 2016
Country/Territory	Canada
City	Vancouver
Period	7/24/16 → 7/29/16

All Science Journal Classification (ASJC) codes

Artificial Intelligence
Modelling and Simulation
Computer Science Applications
Control and Optimization

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10.1109/CEC.2016.7743959

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Accelerating evolutionary computation using estimated convergence points. / Yu, Jun; Pei, Yan; Takagi, Hideyuki.

2016 IEEE Congress on Evolutionary Computation, CEC 2016. Institute of Electrical and Electronics Engineers Inc., 2016. p. 1438-1444 7743959 (2016 IEEE Congress on Evolutionary Computation, CEC 2016).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Yu, J, Pei, Y & Takagi, H 2016, Accelerating evolutionary computation using estimated convergence points. in 2016 IEEE Congress on Evolutionary Computation, CEC 2016., 7743959, 2016 IEEE Congress on Evolutionary Computation, CEC 2016, Institute of Electrical and Electronics Engineers Inc., pp. 1438-1444, 2016 IEEE Congress on Evolutionary Computation, CEC 2016, Vancouver, Canada, 7/24/16. https://doi.org/10.1109/CEC.2016.7743959

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abstract = "We use the convergence points estimated by our proposed method as elite individuals for evolutionary computation and evaluate the acceleration effect and analyze the effect and computational cost. The worst individuals in population are replaced with the convergence points estimated from the moving vectors between parent individuals and their offspring; i.e. these convergence points are used as elite individuals. Differential evolution (DE) and 14 benchmark functions are used in our evaluation experiments. The experimental results show that use of the estimated convergence points as elite can accelerate DE search in spite of the calculation cost of the convergence points. We finally analyze the components of the proposed estimation method to improve cost-performance.",

author = "Jun Yu and Yan Pei and Hideyuki Takagi",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE. Copyright: Copyright 2017 Elsevier B.V., All rights reserved.; 2016 IEEE Congress on Evolutionary Computation, CEC 2016 ; Conference date: 24-07-2016 Through 29-07-2016",

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Accelerating evolutionary computation using estimated convergence points

Abstract

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