Hybrid Metaheuristics Third International Workshop, HM 2006 Gran Canaria, Spain, October 13-14, 2006 Proceedings /

• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Άλλοι συγγραφείς: Almeida, Francisco (Επιμελητής έκδοσης), Blesa Aguilera, María J. (Επιμελητής έκδοσης), Blum, Christian (Επιμελητής έκδοσης), Moreno Vega, José Marcos (Επιμελητής έκδοσης), Pérez Pérez, Melquíades (Επιμελητής έκδοσης), Roli, Andrea (Επιμελητής έκδοσης), Sampels, Michael (Επιμελητής έκδοσης)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Berlin, Heidelberg : Springer Berlin Heidelberg, 2006.
• Σειρά: Lecture Notes in Computer Science, 4030
• Θέματα: Computer science.; Computers.; Algorithms.; Numerical analysis.; Artificial intelligence.; Pattern recognition.; Computer Science.; Artificial Intelligence (incl. Robotics).; Algorithm Analysis and Problem Complexity.; Computation by Abstract Devices.; Numeric Computing.; Pattern Recognition.
• Φυσική περιγραφή: X, 193 p. online resource.
• ISBN: 9783540463856
• ISSN: 0302-9743 ;

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.