Evolution Strategies and Threshold Selection

A hybrid approach that combines the (1 + 1)-ES and threshold selection methods is developed. The framework of the new experimentalism is used to perform a detailed statistical analysis of the effects that are caused by this hybridization. Experimental results on the sphere function indicate that hybridization worsens the performance of the evolution strategy, because evolution strategies are well-scaled hill-climbers: the additional threshold disturbs the self-adaptation process of the evolution strategy. Theory predicts that the hybrid approach might be advantageous in the presence of noise. This effect could be observed-however, a proper fine tuning of the algorithm's parameters appears to be advantageous.

[1]  C. Wright.  Representing and Intervening , 1985 .

[2]  E. C. Stewart,et al.  Study of a global search algorithm for optimal control. , 1967 .

[3]  T. Back,et al.  Thresholding-a selection operator for noisy ES , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[4]  Thomas Bartz-Beielstein,et al.  Tuning search algorithms for real-world applications: a regression tree based approach , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[5]  Thomas Stützle,et al.  Stochastic Local Search: Foundations & Applications , 2004 .

[6]  Thomas Bartz-Beielstein,et al.  Design and Analysis of Optimization Algorithms Using Computational Statistics , 2004 .

[7]  Holger H. Hoos,et al.  Stochastic local search - methods, models, applications , 1998, DISKI.

[8]  Wolfgang Banzhaf,et al.  Total synthesis of algorithmic chemistries , 2005, GECCO '05.

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

[10]  Hans-Paul Schwefel,et al.  Evolution and Optimum Seeking: The Sixth Generation , 1993 .

[11]  Gerhard W. Dueck,et al.  Threshold accepting: a general purpose optimization algorithm appearing superior to simulated anneal , 1990 .

[12]  Thomas Bartz-Beielstein,et al.  New experimentalism applied to evolutionary computation , 2005 .

[13]  Thomas Bartz-Beielstein,et al.  Sequential parameter optimization , 2005, 2005 IEEE Congress on Evolutionary Computation.

[15]  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 .

[16]  I. Hacking,et al.  Representing and Intervening. , 1986 .

[17]  Jeffrey C. Lagarias,et al.  Convergence Properties of the Nelder-Mead Simplex Method in Low Dimensions , 1998, SIAM J. Optim..