Qualms Regarding the Optimality of Cumulative Path Length Control in CSA/CMA-Evolution Strategies

Cumulative step-size adaptation (CSA) based on path length control is regarded as a robust alternative to the standard mutative self-adaptation technique in evolution strategies (ES), guaranteeing an almost optimal control of the mutation operator. This paper shows that the underlying basic assumption in CSA the perpendicularity of expected consecutive steps does not necessarily guarantee optimal progress performance for (/I) intermediate recombinative ES

[1]  N. Hansen,et al.  Convergence Properties of Evolution Strategies with the Derandomized Covariance Matrix Adaptation: T , 1997 .

[2]  Laxmikant V. Kale,et al.  Parallel problem solving , 1990 .

[3]  Thomas Bäck,et al.  Evolutionary computation: comments on the history and current state , 1997, IEEE Trans. Evol. Comput..

[4]  Nikolaus Hansen,et al.  Completely Derandomized Self-Adaptation in Evolution Strategies , 2001, Evolutionary Computation.

[5]  Hans-Georg Beyer,et al.  Performance analysis of evolutionary optimization with cumulative step length adaptation , 2004, IEEE Transactions on Automatic Control.

[6]  Thomas Bäck,et al.  An Overview of Evolutionary Algorithms for Parameter Optimization , 1993, Evolutionary Computation.

[7]  Nikolaus Hansen,et al.  Adapting arbitrary normal mutation distributions in evolution strategies: the covariance matrix adaptation , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[8]  Hans-Georg Beyer,et al.  The Theory of Evolution Strategies , 2001, Natural Computing Series.

[9]  Ingo Rechenberg,et al.  Evolutionsstrategie : Optimierung technischer Systeme nach Prinzipien der biologischen Evolution , 1973 .

[10]  H. Beyer,et al.  Some observations on the interaction of recombination and self-adaptation in evolution strategies , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[11]  N. Hansen,et al.  Step-Size Adaptation Based on Non-Local Use Selection Information , 1994 .

[12]  Hans-Georg Beyer,et al.  Local performance of the (1 + 1)-ES in a noisy environment , 2002, IEEE Trans. Evol. Comput..

[13]  H. Beyer.  An alternative explanation for the manner in which genetic algorithms operate. , 1997, Bio Systems.

[14]  Nikolaus Hansen,et al.  A Derandomized Approach to Self-Adaptation of Evolution Strategies , 1994, Evolutionary Computation.

[15]  Hans-Georg Beyer,et al.  Toward a Theory of Evolution Strategies: Self-Adaptation , 1995, Evolutionary Computation.

[16]  Nikolaus Hansen,et al.  Step-Size Adaption Based on Non-Local Use of Selection Information , 1994, PPSN.

[17]  Hans-Georg Beyer,et al.  Local Performance of the (μ/μ, μ)-ES in a Noisy Environment , 2000, FOGA.

[18]  Zbigniew Michalewicz,et al.  Evolutionary Computation 2 , 2000 .

[19]  W. Vent,et al.  Rechenberg, Ingo, Evolutionsstrategie — Optimierung technischer Systeme nach Prinzipien der biologischen Evolution. 170 S. mit 36 Abb. Frommann‐Holzboog‐Verlag. Stuttgart 1973. Broschiert , 1975 .

[20]  Hans-Georg Beyer,et al.  Performance analysis of evolution strategies with multi-recombination in high-dimensional RN-search spaces disturbed by noise , 2002, Theor. Comput. Sci..

[21]  Zbigniew Michalewicz,et al.  Evolutionary Computation 1 , 2018 .

[22]  Hans-Georg Beyer,et al.  Efficiency and mutation strength adaptation of the (μ/μI, λ)-ES in a noisy environment , 2000 .

[23]  Hans-Georg Beyer,et al.  Efficiency and Mutation Strength Adaptation of the (mu, muI, lambda)-ES in a Noisy Environment , 2000, PPSN.

[24]  Nikolaus Hansen,et al.  Verallgemeinerte individuelle Schrittweitenregelung in der Evolutionsstrategie , 1998 .