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Improved Running Time Analysis of the (1+1)-ES on the Sphere Function

  • Wu Jiang
  • Chao Qian
  • Ke Tang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10954)

Abstract

During the last two decades, much progress has been achieved on the running time analysis (one essential theoretical aspect) of evolutionary algorithms (EAs). However, most of them focused on discrete optimization, and the theoretical understanding is largely insufficient for continuous optimization. The few studies on evolutionary continuous optimization mainly analyzed the running time of the (1+1)-ES with Gaussian and uniform mutation operators solving the sphere function, the known bounds of which are, however, quite loose compared with the empirical observations. In this paper, we significantly improve their lower bound, i.e., from \( \varOmega (n) \) to \( \varOmega (e^{cn} ) \). Then, we study the effectiveness of 1/5-rule, a widely used self-adaptive strategy, for continuous EAs using uniform mutation operator for the first time. We prove that for the (1+1)-ES with uniform mutation operator solving the sphere function, using 1/5-rule can reduce the running time from exponential to polynomial.

Keywords

Running time analysis Continuous optimization Evolution strategies 

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Anhui Province Key Lab of Big Data Analysis and ApplicationUniversity of Science and Technology of ChinaHefeiChina
  2. 2.Shenzhen Key Lab of Computational IntelligenceSouthern University of Science and TechnologyShenzhenChina

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