Evolutionary adaptation of the differential evolution control parameters

This papers proposes a novel self-adaptive scheme for the evolution of crucial control parameters in Evolutionary Algorithms. More specifically, we suggest to utilize the Differential Evolution algorithm to endemically evolve its own control parameters. To achieve this, two simultaneous instances of Differential Evolution are used, one of which is responsible for the evolution of the crucial user-defined mutation and recombination constants. This self-adaptive Differential Evolution algorithm alleviates the need of tuning these user-defined parameters while maintains the convergence properties of the original algorithm. The evolutionary self-adaptive scheme is evaluated through several well-known optimization benchmark functions and the experimental results indicate that the proposed approach is promising.

[1]  A. E. Eiben,et al.  Introduction to Evolutionary Computing , 2003, Natural Computing Series.

[2]  Jouni Lampinen,et al.  A Fuzzy Adaptive Differential Evolution Algorithm , 2005, Soft Comput..

[3]  Jouni Lampinen,et al.  A Trigonometric Mutation Operation to Differential Evolution , 2003, J. Glob. Optim..

[4]  A. Griewank.  Generalized descent for global optimization , 1981 .

[5]  Sandro Ridella,et al.  Minimizing multimodal functions of continuous variables with the “simulated annealing” algorithmCorrigenda for this article is available here , 1987, TOMS.

[6]  A. Kai Qin,et al.  Self-adaptive Differential Evolution Algorithm for Constrained Real-Parameter Optimization , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[7]  René Thomsen,et al.  A comparative study of differential evolution, particle swarm optimization, and evolutionary algorithms on numerical benchmark problems , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[8]  A. V. Levy,et al.  Topics in global optimization , 1982 .

[9]  Rainer Storn,et al.  System design by constraint adaptation and differential evolution , 1999, IEEE Trans. Evol. Comput..

[10]  Dimitris K. Tasoulis,et al.  Parallel differential evolution , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[11]  Jason Teo,et al.  Exploring dynamic self-adaptive populations in differential evolution , 2006, Soft Comput..

[12]  Vassilis P. Plagianakos,et al.  Neural network training with constrained integer weights , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[13]  Dimitris K. Tasoulis,et al.  Human Designed Vs. Genetically Programmed Differential Evolution Operators , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[14]  H. Abbass.  The self-adaptive Pareto differential evolution algorithm , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[15]  A. Kai Qin,et al.  Self-adaptive differential evolution algorithm for numerical optimization , 2005, 2005 IEEE Congress on Evolutionary Computation.

[16]  Janez Brest,et al.  Performance comparison of self-adaptive and adaptive differential evolution algorithms , 2007, Soft Comput..

[17]  Dimitris K. Tasoulis,et al.  Clustering in evolutionary algorithms to efficiently compute simultaneously local and global minima , 2005, 2005 IEEE Congress on Evolutionary Computation.

[18]  Kenneth Alan De Jong,et al.  An analysis of the behavior of a class of genetic adaptive systems. , 1975 .

[19]  Uday K. Chakraborty,et al.  Advances in Differential Evolution , 2010 .

[20]  Andries Petrus Engelbrecht,et al.  Empirical analysis of self-adaptive differential evolution , 2007, Eur. J. Oper. Res..

[21]  Jeffrey Horn,et al.  Handbook of evolutionary computation , 1997 .

[22]  Vassilis P. Plagianakos,et al.  Parallel evolutionary training algorithms for “hardware-friendly” neural networks , 2002, Natural Computing.

[23]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[24]  Zbigniew Michalewicz,et al.  Handbook of Evolutionary Computation , 1997 .

[25]  Michael N. Vrahatis,et al.  Recent approaches to global optimization problems through Particle Swarm Optimization , 2002, Natural Computing.

[26]  R. Storn,et al.  Differential Evolution - A simple and efficient adaptive scheme for global optimization over continuous spaces , 2004 .

[27]  Thomas Bäck,et al.  Adaptive business intelligence based on evolution strategies: some application examples of self-adaptive software , 2002, Inf. Sci..

[28]  Michael G. Epitropakis,et al.  Balancing the exploration and exploitation capabilities of the Differential Evolution Algorithm , 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).

[29]  R. Storn,et al.  Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series) , 2005 .

[30]  K. Dejong,et al.  An analysis of the behavior of a class of genetic adaptive systems , 1975 .