The impatience mechanism as a diversity maintaining and saddle crossing strategy

Author:

Karcz-Duleba Iwona1

Affiliation:

1. Department of Control and Mechatronics, Faculty of Electronics Wrocław University of Science and Technology, Wyb. Wyspiańskiego 27, 50-370 Wrocław, Poland

Abstract

Abstract The impatience mechanism diversifies the population and facilitates escaping from a local optima trap by modifying fitness values of poorly adapted individuals. In this paper, two versions of the impatience mechanism coupled with a phenotypic model of evolution are studied. A population subordinated to a basic version of the impatience mechanism polarizes itself and evolves as a dipole centered around an averaged individual. In the modified version, the impatience mechanism is supplied with extra knowledge about a currently found optimum. In this case, the behavior of a population is quite different than previously-considerable diversification is also observed, but the population is not polarized and evolves as a single cluster. The impatience mechanism allows crossing saddles relatively fast in different configurations of bimodal and multimodal fitness functions. Actions of impatience mechanisms are shown and compared with evolution without the impatience and with a fitness sharing. The efficiency of crossing saddles is experimentally examined for different fitness functions. Results presented in the paper confirm good properties of the impatience mechanism in diversity maintaining and saddle crossing.

Publisher

Walter de Gruyter GmbH

Subject

Applied Mathematics,Engineering (miscellaneous),Computer Science (miscellaneous)

Reference23 articles.

1. Barabasz, B., Gajda-Zagórska, E., Migórski, S., Paszyński, M., Schaefer, R. and Smołka, M. (2014). A hybrid algorithm for solving inverse problems in elasticity, International Journal of Applied Mathematics and Computer Science 24(4): 865-886, DOI: 10.2478/amcs-2014-0064.

2. Chen, T., He, J., Chen, G. and Yao, X. (2010). Choosing selection pressure for wide-gap problems, Theoretical Computer Science 411(6): 926-934.

3. Chorazyczewski, A. and Galar, R. (1998). Visualization of evolutionary adaptation in Rn, in V. Porto et al. (Eds.), Evolutionary Programming VII, Springer-Verlag, London, pp. 657-668.

4. DeJong, K. (1975). An Analysis of the Behavior of a Class of Genetic Adaptive Systems, Ph.D. thesis, University of Michigan, Ann Arbour, MI.

5. Dick, G. and Whigham, P.A. (2006). Spatially-structured evolutionary algorithms and sharing: Do they mix?, in Wang et al. (Eds.), SEAL 2006, Lecture Notes in Computer Science, Vol. 4247, Springer-Verlag, Berlin/Heidelberg, pp. 457-464.

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3