Computational results for an automatically tuned CMA-ES with increasing population size on the CEC’05 benchmark set

Adenso-Diaz B, Laguna M (2006) Fine-tuning of algorithms using fractional experimental designs and local search. Operations Res 54:99–114 Auger A, Hansen N (2005) A restart CMA evolution strategy with increasing population size. In: Proceeding of IEEE Congress on Evolutionary Computation, CEC’05. IEEE Press, Piscataway, pp 1769–1776 Balaprakash P, Birattari M, Stützle T (2007) Improvement strategies for the F-Race algorithm: sampling design and iterative refinement. In: Bartz-Beielstein et al. (eds) Proceedings of International Conference on Hybrid Metaheuristics, LNCS, vol 4771. Springer, Berlin pp 108–122 Bartz-Beielstein T (2006) Experimental research in evolutionary computation: the new experimentalism. Springer, Berlin Birattari M (2009) Tuning metaheuristics: a machine learning perspective, 1st edn. Springer, Berlin Birattari M, Stützle T, Paquete L, Varrentrapp K (2002) A racing algorithm for configuring metaheuristics. In: Proceedings of the Genetic and Evolutionary Computation Conference, GECCO’02. Morgan Kaufmann, San Francisco, pp 11–18 Birattari M, Yuan Z, Balaprakash P, Stützle T (2010) F-Race and Iterated F-Race: an overview. In: Bartz-Beielstein et al. (eds) Experimental methods for the analysis of optimization algorithms. Springer, Berlin, pp 311–336 Hansen N (2010) The CMA evolution strategy: a tutorial. http://www.lri.fr/~hansen/cmatutorial.pdf Hansen N, Ostermeier A (1996) Adapting arbitrary normal mutation distributions in evolution strategies: the covariance matrix adaptation. In: Proceedings of IEEE International Conference on Evolutionary Computation, CEC’96. IEEE Press, Piscataway, pp 312–317 Hansen N, Ostermeier A (2001) Completely derandomized self-adaptation in evolution strategies. Evolutionary Comput 9(2):159–195 Hansen N, Muller SD, Koumoutsakos P (2003) Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (CMA-ES). Evolutionary Comput 11(1):1–18 Herrera F, Lozano M, Molina D (2010) Test suite for the special issue of soft computing on scalability of evolutionary algorithms and other metaheuristics for large scale continuous optimization problems. http://sci2s.ugr.es/eamhco/ Hoos HH, Stützle T (2004) Stochastic local search: foundations applications. Morgan Kaufmann, San Francisco Hutter F, Babic D, Hoos HH, Hu AJ (2007) Boosting verification by automatic tuning of decision procedures. In: Proceedings of Formal Methods in Computer Aided Design, FMCAD’07. IEEE Press, Piscataway, pp 27–34 Hutter F, Hoos HH, Leyton-Brown K, Murphy K (2009a) An experimental investigation of model-based parameter optimisation: SPO and beyond. In: Proceedings of the Genetic and Evolutionary Computation Conference, GECCO’09. ACM, New York, pp 271–278 Hutter F, Hoos HH, Leyton-Brown K, Stützle T (2009b) ParamILS: an automatic algorithm configuration framework. J Artif Intell Res 36(1):267–306 Liao T, Molina D, Montes de Oca MA, Stützle T (2011a) A note on the effects of enforcing bound constraints on algorithm comparisons using the IEEE CEC’05 Benchmark Function Suite. Tech. Rep. TR/IRIDIA/2011-010, IRIDIA. Université Libre de Bruxelles, Belgium Liao T, Montes de Oca MA, Stützle T (2011b) Computational results for an automatically tuned CMA-ES with increasing population size on the CEC’05 benchmark set. http://iridia.ulb.ac.be/supp/IridiaSupp2011-023 Liao T, Montes de Oca MA, Stützle T (2011c) Tuning parameters across mixed dimensional instances: a performance scalability study of Sep-G-CMA-ES. In: Proceedings of the Workshop on Scaling Behaviours of Landscapes, Parameters and Algorithms of the Genetic and Evolutionary Computation Conference. GECCO’11, ACM, NY, pp 703–706 López-Ibáñez M, Dubois-Lacoste J, Stützle T, Birattari M (2011) The irace package, iterated race for automatic algorithm configuration Tech. Rep. TR/IRIDIA/2011-004, IRIDIA, Université Libre de Bruxelles, Belgium Lozano M, Molina D, Herrera F (2011) Editorial scalability of evolutionary algorithms and other metaheuristics for large-scale continuous optimization problems. Soft computing—a Fusion of Foundations. Methodol Appl 15:2085–2087 Mersmann O, Bischl B, Trautmann H, Preuss M, Weihs C, Rudolph G (2011) Exploratory landscape analysis. In: Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 11. ACM, NY, pp 829–836 Molina D, Lozano M, García-Martínez C, Herrera F (2010) Memetic algorithms for continuous optimisation based on local search chains. Evolutionary Comput 18(1):27–63 Müller CL, Baumgartner B, Sbalzarini IF (2009) Particle swarm CMA evolution strategy for the optimization of multi-funnel landscapes. In: Proceeding of IEEE Congress on Evolutionary Computation, CEC 09. IEEE Press, Piscataway, pp 2685–2692 Nannen V, Eiben AE (2007) Relevance estimation and value calibration of evolutionary algorithm parameters. In: Proceedings of the 20th International Joint Conference on Artifical Intelligence. Morgan Kaufmann, San Francisco, pp 975–980 Ros R (2009) Benchmarking sep-CMA-ES on the BBOB- 2009 noisy testbed. In: Proceedings of the Conference Companion on Genetic and Evolutionary Computation Conference: late breaking papers. GECCO 09. ACM, NY , pp 2441–2446 Ros R, Hansen N (2008) A simple modification in CMA-ES achieving linear time and space complexity. In: Rudolph G et al. (eds) Parallel problem solving from nature PPSN X, vol 5199. Springer, Berlin, LNCS, pp 296–305 Smit SK, Eiben AE (2010) Beating the world champion evolutionary algorithm via REVAC tuning. In: Proceeding of IEEE Congress on Evolutionary Computation, CEC 10. IEEE Press, Piscataway, pp 1–8 Suganthan PN, Hansen N, Liang JJ, Deb K, Chen Y, Auger A, Tiwari S (2005) Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization. Tech. Rep. 2005005, Nanyang Technological University