Scalability of generalized adaptive differential evolution for large-scale continuous optimization

Auger A, Hansen N (2005) A restart CMA evolution strategy with increasing population size. In: Proceedings of the 2005 IEEE congress on evolutionary computation, pp 1769–1776 Brest J, Greiner S, Boskovic B, Mernik M, Žumer V (2006) Self-adapting control parameters in differential evolution: a comparative study on numerical benchmark problems. IEEE Trans Evol Comput 10(6):646–657 Benchmark functions for the Special Issue of Soft Computing. http://sci2s.ugr.es/eamhco/updated-functions1-19.pdf Components and Parameters of DE, Real-coded CHC, and G-CMA-ES. http://sci2s.ugr.es/eamhco/descriptions.pdf Das S, Abraham A, Chakraborty UK, Konar A (2009) Differential evolution using a neighborhood-based mutation operator. IEEE Trans Evol Comput 13(3):526–553 Eshelman LJ, Schaffer JD (1993) Real-coded genetic algorithms and interval-schemata. In: Whitley LD, (ed), Foundations of genetic algorithms 2. Morgan Kaufmann, pp 187–202 Eshelman LJ (1991) The CHC adaptive search algorithm: How to have safe search when engaging in nontraditional genetic recombination. Found Genetic Algorithms 1:265–283 Eshelman LJ, Schaffer JD (1993) Real-coded genetic algorithms and interval-schemata. Found Genetic Algorithms 2(1993):187–202 Experimental results of differential evolution with exponential crossover and binomial crossover. http://sci2s.ugr.es/eamhco/decross_values.xls García S, Molina D, Lozano M, Herrera F (2009) A study on the use of non-parametric tests for analyzing the evolutionary algorithms behaviour: a case study on the CEC2005 special session on real parameter optimization. J Heuristics 15(6):617–644 Hansen N (2005) Compilation of results on the CEC benchmark function set, technical report, vol 13. Institute of Computational Science, ETH Zurich, Switerland Hansen N, Ostermeier A (2001) Completely derandomized self-adaptation in evolution strategies. Evol Comput 9(2):159–195 Hansen N, Kern S (2004) Evaluating the CMA evolution strategy on multimodal test functions. In: Proceedings of the eighth international conference on parallel problem solving from nature (PPSN VIII), pp 282–291 Liu Y, Yao X, Zhao Q, Higuchi T (2001) Scaling up fast evolutionary programming with cooperative coevolution. In: Proceedings of the 2001 congress on evolutionary computation, pp 1101–1108 Lee CY, Yao X (2004) Evolutionary programming using mutations based on the Lévy probability distribution. IEEE Trans Evol Comput 8(1):1–13 Neri F, Tirronen V (2010) Recent advances in differential evolution: a survey and experimental analysis. Artif Intell Rev 33(1):61–106 Price KV, Storn RM, Lampinen JA (2005) Differential evolution: a practical approach to global optimization. Springer, Berlin. ISBN:3-540-20950-6 Peng F, Tang K, Chen G, Yao X (2009) Population-based algorithm portfolios for numerical optimization. IEEE Trans Evol Comput (in press) Qin AK, Suganthan PN (2005) Self-adaptive differential evolution algorithm for numerical optimization. In: Proceedings of the 2005 IEEE congress on evolutionary computation, vol 2, pp 1785–1791 Qin AK, Huang VL, Suganthan PN (2009) Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Trans Evol Comput 13(2):398–417 Storn R, Price K (1997) Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J Global Optimization 11(4):341–359 Storn R (1999) System design by constraint adaptation and differential evolution. IEEE Trans Evol Comput 3(1):22–34 Tang K, Li X, Suganthan PN, Yang Z, Weise T (2009) Benchmark Functions for the CEC2010 Special session and competition on large-scale global optimization, technical report. Nature Inspired Computation and Applications Laboratory, University of Science and Technology of China, China. http://nical.ustc.edu.cn/cec10ss.php Vesterstrom J, Thomsen R (2004) A comparative study of differential evolution, particle swarm optimization, and evolutionary algorithms on numerical benchmark problems. In: Proceedings of the 2004 IEEE congress on evolutionary computation, vol 2, pp 1980–1987 Whitley D, Rana S, Dzubera J, Mathias KE (1996) Evaluating evolutionary algorithms. Artif Intell 85(1–2):245–276 Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1):67–82 Yang Z, Tang K, Yao X (2008) Large scale evolutionary optimization using cooperative coevolution. Inf Sci 178(15):2985–2999 Yang Z, He J, Yao X (2008) Making a difference to differential evolution. In: Michalewicz Z, Siarry P, (eds) Advances in metaheuristics for hard optimization. Springer, Berlin, pp 397–414 Yang Z, Tang K, Yao X (2008) Self-adaptive differential evolution with neighborhood search. In: Proceedings of the 2008 IEEE congress on evolutionary computation, pp 1110–1116 Yang Z, Zhang J, Tang K, Yao X, Sanderson AC (2009) An adaptive coevolutionary differential evolution algorithm for large-scale optimization. In: Proceedings of the 2009 IEEE congress on evolutionary computation, pp 102–109 Yao X, Liu Y, Lin G (1999) Evolutionary programming made faster. IEEE Trans Evol Comput 3(2):82–102 Zaharie D (2002) Critical values for the control parameters of differential evolution algorithms. In: Proceedings of the 8th international conference on soft computing, pp 62–67 Zhang J, Sanderson AC (2009) JADE: adaptive differential evolution with optional external archive. IEEE Trans Evol Comput 13(5):945–958