Investigating the impact of alternative evolutionary selection strategies on multi-method global optimization

Algorithm selection is an important consideration in multi-method global optimization. This paper investigates the use of various algorithm selection strategies derived from well known evolutionary selection mechanisms. Selection strategy performance is evaluated on a diverse set of floating point benchmark problems and meaningful conclusions are drawn with regard to the impact of selective pressure on algorithm selection in a multi-method environment.

[1]  Álvaro Fialho,et al.  Adaptive strategy selection in differential evolution , 2010, GECCO '10.

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

[3]  Graham Kendall,et al.  A Tabu-Search Hyperheuristic for Timetabling and Rostering , 2003, J. Heuristics.

[4]  Michèle Sebag,et al.  Fitness-AUC bandit adaptive strategy selection vs. the probability matching one within differential evolution: an empirical comparison on the bbob-2010 noiseless testbed , 2010, GECCO '10.

[5]  Graham Kendall,et al.  A Classification of Hyper-heuristic Approaches , 2010 .

[6]  Bruce A. Robinson,et al.  Self-Adaptive Multimethod Search for Global Optimization in Real-Parameter Spaces , 2009, IEEE Transactions on Evolutionary Computation.

[7]  Andries Petrus Engelbrecht,et al.  Alternative hyper-heuristic strategies for multi-method global optimization , 2010, IEEE Congress on Evolutionary Computation.

[8]  William E. Hart,et al.  Recent Advances in Memetic Algorithms , 2008 .

[9]  Nikolaus Hansen,et al.  A restart CMA evolution strategy with increasing population size , 2005, 2005 IEEE Congress on Evolutionary Computation.

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

[11]  Andries Petrus Engelbrecht,et al.  An analysis of heterogeneous cooperative algorithms , 2009, 2009 IEEE Congress on Evolutionary Computation.

[12]  Edmund K. Burke,et al.  A simulated annealing based hyperheuristic for determining shipper sizes for storage and transportation , 2007, Eur. J. Oper. Res..

[13]  A. Engelbrecht,et al.  A new locally convergent particle swarm optimiser , 2002, IEEE International Conference on Systems, Man and Cybernetics.

[14]  J. Kennedy,et al.  Population structure and particle swarm performance , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[15]  Fei Peng,et al.  Population-Based Algorithm Portfolios for Numerical Optimization , 2010, IEEE Transactions on Evolutionary Computation.