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Meta-Heuristics: Advances and Trends in Local Search Paradigms for
Optimizations comprises a carefully refereed selection of extended
versions of the best papers presented at the Second Meta-Heuristics
Conference (MIC 97). The selected articles describe the most recent
developments in theory and applications of meta-heuristics,
heuristics for specific problems, and comparative case studies. The
book is divided into six parts, grouped mainly by the techniques
considered. The extensive first part with twelve papers covers tabu
search and its application to a great variety of well-known
combinatorial optimization problems (including the
resource-constrained project scheduling problem and vehicle routing
problems). In the second part we find one paper where tabu search
and simulated annealing are investigated comparatively and two
papers which consider hybrid methods combining tabu search with
genetic algorithms. The third part has four papers on genetic and
evolutionary algorithms. Part four arrives at a new paradigm within
meta-heuristics. The fifth part studies the behavior of parallel
local search algorithms mainly from a tabu search perspective. The
final part examines a great variety of additional meta-heuristics
topics, including neural networks and variable neighbourhood search
as well as guided local search. Furthermore, the integration of
meta-heuristics with the branch-and-bound paradigm is investigated.
Meta-Heuristics: Advances and Trends in Local Search Paradigms for
Optimizations comprises a carefully refereed selection of extended
versions of the best papers presented at the Second Meta-Heuristics
Conference (MIC 97). The selected articles describe the most recent
developments in theory and applications of meta-heuristics,
heuristics for specific problems, and comparative case studies. The
book is divided into six parts, grouped mainly by the techniques
considered. The extensive first part with twelve papers covers tabu
search and its application to a great variety of well-known
combinatorial optimization problems (including the
resource-constrained project scheduling problem and vehicle routing
problems). In the second part we find one paper where tabu search
and simulated annealing are investigated comparatively and two
papers which consider hybrid methods combining tabu search with
genetic algorithms. The third part has four papers on genetic and
evolutionary algorithms. Part four arrives at a new paradigm within
meta-heuristics. The fifth part studies the behavior of parallel
local search algorithms mainly from a tabu search perspective. The
final part examines a great variety of additional meta-heuristics
topics, including neural networks and variable neighbourhood search
as well as guided local search. Furthermore, the integration of
meta-heuristics with the branch-and-bound paradigm is investigated.
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