Analytical estimation of the convergence point of populations

Noboru Murata; Ryuei Nishii; Hideyuki Takagi; Yan Pei

doi:10.1109/CEC.2015.7257211

Analytical estimation of the convergence point of populations

Noboru Murata, Ryuei Nishii, Hideyuki Takagi, Yan Pei

Division of Advanced Mathematics Technology

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

13 Citations (Scopus)

Abstract

We propose methods of estimating the convergence point for the moving vectors of individuals between generations or evolution paths and show that the estimated convergence point can be useful information for accelerating evolutionary computation (EC). As the first stage of this new approach, we do not combine the proposed methods with EC search in this paper, but rather evaluate how power an individual the the estimated convergence point is by comparing fitness values. Through experimental evaluations, we show that the estimated point can be a powerful elite for unimodal fitness landscapes and that clustering moving vectors according to the aimed points is the next research target for multimodal fitness landscape.

Original language	English
Title of host publication	2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2619-2624
Number of pages	6
ISBN (Electronic)	9781479974924
DOIs	https://doi.org/10.1109/CEC.2015.7257211
Publication status	Published - Sept 10 2015
Event	IEEE Congress on Evolutionary Computation, CEC 2015 - Sendai, Japan Duration: May 25 2015 → May 28 2015

Publication series

Name	2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings

Other

Other	IEEE Congress on Evolutionary Computation, CEC 2015
Country/Territory	Japan
City	Sendai
Period	5/25/15 → 5/28/15

All Science Journal Classification (ASJC) codes

Computer Science Applications
Computational Mathematics

Access to Document

10.1109/CEC.2015.7257211

Cite this

Murata, N., Nishii, R., Takagi, H., & Pei, Y. (2015). Analytical estimation of the convergence point of populations. In 2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings (pp. 2619-2624). Article 7257211 (2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CEC.2015.7257211

Analytical estimation of the convergence point of populations. / Murata, Noboru; Nishii, Ryuei; Takagi, Hideyuki et al.
2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2015. p. 2619-2624 7257211 (2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings).

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

Murata, N, Nishii, R, Takagi, H & Pei, Y 2015, Analytical estimation of the convergence point of populations. in 2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings., 7257211, 2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 2619-2624, IEEE Congress on Evolutionary Computation, CEC 2015, Sendai, Japan, 5/25/15. https://doi.org/10.1109/CEC.2015.7257211

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AB - We propose methods of estimating the convergence point for the moving vectors of individuals between generations or evolution paths and show that the estimated convergence point can be useful information for accelerating evolutionary computation (EC). As the first stage of this new approach, we do not combine the proposed methods with EC search in this paper, but rather evaluate how power an individual the the estimated convergence point is by comparing fitness values. Through experimental evaluations, we show that the estimated point can be a powerful elite for unimodal fitness landscapes and that clustering moving vectors according to the aimed points is the next research target for multimodal fitness landscape.

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Analytical estimation of the convergence point of populations

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