Titlebook: Evolutionary Computation in Combinatorial Optimization; 11th European Confer Peter Merz,Jin-Kao Hao Conference proceedings 2011 Springer Be

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Conclusions and some Suggestions for Reform,ocess, adapting parameter settings on an instance-per-instance basis, and possibly to each phase of the search. The results suggest that off-line tuning achieves a better performance than on-line tuning.

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Fitness-Probability Cloud and a Measure of Problem Hardness for Evolutionary Algorithms, To illustrate the effectiveness of our approach, we apply it to four test problems: OneMax, Trap, OneMix and Subset Sum. We then contrast the predictions made by the . to the actual performance measured using the number of fitness evaluations. The results suggest that the new measure can reliably indicate problem hardness for EAs.

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Frequency Distribution Based Hyper-Heuristic for the Bin-Packing Problem,nerated to avoid getting trapped into the local optima. The experimental results on the bin-packing problems show that FDHH can obtain optimal solutions on more instances than the original hyper-heuristic.

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0302-9743 pean Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2011, held in Torino, Italy, in April 2011. The 22 revised full papers presented were carefully reviewed and selected from 42 submissions. The papers present the latest research and discuss current developments and app

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E. Millich,J. P. Joulia,D. Van Asselt of running time and number of optimal solutions found. The experimental results indicate that this simple local search algorithm is able to find a representative set of optimal solutions in most of the cases, and in much less time than exact approaches.