The book Scatter Search by Manuel Laguna and Rafael Martí represents a long-awaited "missing link" in the literature of evolutionary methods. Scatter Search (SS)-together with its generalized form called Path Relinking-constitutes the only evolutionary approach that embraces a collection of principles from Tabu Search (TS), an approach popularly regarded to be divorced from evolutionary procedures. The TS perspective, which is responsible for introducing adaptive memory strategies into the metaheuristic literature (at purposeful level beyond simple inheritance mechanisms), may at first seem to be at odds with population-based approaches. Yet this perspective equips SS with a remarkably effective foundation for solving a wide range of practical problems. The successes documented by Scatter Search come not so much from the adoption of adaptive memory in the range of ways proposed in Tabu Search (except where, as often happens, SS is advantageously coupled with TS), but fromthe use of strategic ideas initially proposed for exploiting adaptive memory, which blend harmoniously with the structure of Scatter Search. From a historical perspective, the dedicated use of heuristic strategies both to guide the process of combining solutions and to enhance the quality of offspring has been heralded as a key innovation in evolutionary methods, giving rise to what are sometimes called "hybrid" (or "memetic") evolutionary procedures. The underlying processes have been introduced into the mainstream of evolutionary methods (such as genetic algorithms, for example) by a series of gradual steps beginning in the late 1980s.
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Abraham Duarte is an Associate Professor in the Computer Science Department at the Rey Juan Carlos University(Madrid, Spain). He received his doctoral degree in Computer Sciences from the Rey Juan Carlos University. His research is devoted to the development of models and solution methods based on meta-heuristics for combinatorial optimization problems. He has published more than 50 papers in SCI-JCR prestigious scientific journals such us European Journal of Operational Research, INFORMS Journal on Computing, Computational Optimization and Applications, or Computers & Operations Research. Dr. Duarte has also published 11 papers in non-indexed journals, 20 book chapters and more than 70 papers in conference proceedings. He has been the principal investigator on 5 competitive research projects with a total budget of 200000 euros (aprox.), he has been the advisor of 5 doctoral theses, and he is co-inventor of a US patent (US 20100138373 A1). Dr Duarte is reviewer of the Journal of Heuristic, Journal of Mathematical Modeling and Algorithms, INFORMS Journal on Computing, Applied Soft Computing, European Journal of Operational Research and Soft Computing. He is also member of the program committee of the conferences MAEB, HIS, ISDA, SOCO or MHIPL.
The book Scatter Search by Manuel Laguna and Rafael Martí represents a long-awaited "missing link" in the literature of evolutionary methods. Scatter Search (SS)-together with its generalized form called Path Relinking-constitutes the only evolutionary approach that embraces a collection of principles from Tabu Search (TS), an approach popularly regarded to be divorced from evolutionary procedures. The TS perspective, which is responsible for introducing adaptive memory strategies into the metaheuristic literature (at purposeful level beyond simple inheritance mechanisms), may at first seem to be at odds with population-based approaches. Yet this perspective equips SS with a remarkably effective foundation for solving a wide range of practical problems. The successes documented by Scatter Search come not so much from the adoption of adaptive memory in the range of ways proposed in Tabu Search (except where, as often happens, SS is advantageously coupled with TS), but from the use of strategic ideas initially proposed for exploiting adaptive memory, which blend harmoniously with the structure of Scatter Search. From a historical perspective, the dedicated use of heuristic strategies both to guide the process of combining solutions and to enhance the quality of offspring has been heralded as a key innovation in evolutionary methods, giving rise to what are sometimes called "hybrid" (or "memetic") evolutionary procedures. The underlying processes have been introduced into the mainstream of evolutionary methods (such as genetic algorithms, for example) by a series of gradual steps beginning in the late 1980s.
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