The International Workshop on Hybrid Metaheuristics reached its third edition with HM 2006. The active and successful participation in the past editions was a clear indication that the research community on metaheuristics and related areas felt the need for a forum to discuss speci?c aspects of hybridization of metaheuristics. The selection of papers for HM 2006 consolidated some of the mainstream issues that have emerged from the past editions. Firstly, there are prominent examples of e?ective hybrid techniques whose design and implementation were motivated by challenging real-world applications. We believe this is particularly important for two reasons: on the one hand, researchers are conscious that the primary goal of developing algorithms is to solve relevant real-life problems; on the other hand, the path towarde?cient solving methods for practical problems is a source of new outstanding ideas and theories. A second important issue is that the research community on metaheur- tics has become increasingly interested in and open to techniques and methods known from arti?cial intelligence (AI) and operations research (OR). So far, the most representative examples of such integration have been the use of AI/OR techniques as subordinates of metaheuristic methods. As a historical and - ymological note, this is in perfect accordance with the original meaning of a metaheuristic as a “general strategy controlling a subordinate heuristic. ” The awareness of the need for a sound experimental methodology is a third keypoint.

Synopsis

This book constitutes the refereed proceedings of the Third International Workshop on Hybrid Metaheuristics, HM 2006, held in Gran Canaria, Spain, in October 2006. The 13 revised full papers presented together with 1 invited paper were carefully reviewed and selected from 42 submissions. Topics of this new emerging field addressed by the papers are novel combinations of components from different metaheuristics, hybridization of metaheuristics and Al/OR techniques, low-level hybridization, high-level hybridization, portfolio techniques, expert systems, co-operative search, taxonomy, terminology, classification of hybrid metaheuristics, co-evolution techniques, automated parameter tuning, empirical and statistical comparison, theoretic aspects of hybridization, parallelization, and software libraries.

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