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MARC 21

Handbook of Metaheuristics
Tag	Description
020	$a9780306480560$9978-0-306-48056-0
082	$a519.6$223
099	$aOnline resource: Springer
245	$aHandbook of Metaheuristics$h[EBook] /$cedited by Fred Glover, Gary A. Kochenberger.
260	$aBoston, MA :$bSpringer US,$c2003.
300	$aXII, 557 p.$bonline resource.
336	$atext$btxt$2rdacontent
337	$acomputer$bc$2rdamedia
338	$aonline resource$bcr$2rdacarrier
440	$aInternational Series in Operations Research & Management Science,$x0884-8289 ;$v57
505	$aScatter Search and Path Relinking: Advances and Applications -- An Introduction to Tabu Search -- Genetic Algorithms -- Genetic Programming: Automatic Synthesis of Topologies and Numerical Parameters -- A Gentle Introduction to Memetic Algorithms -- Variable Neighborhood Search -- Guided Local Search -- Greedy Randomized Adaptive Search Procedures -- The Ant Colony Optimization Metaheuristic: Algorithms, Applications, and Advances -- The Theory and Practice of Simulated Annealing -- Iterated Local Search -- Multi-Start Methods -- Local Search and Constraint Programming -- Constraint Satisfaction -- Artificial Neural Networks for Combinatorial Optimization -- Hyper-Heuristics: An Emerging Direction in Modern Search Technology -- Parallel Strategies for Meta-Heuristics -- Metaheuristic Class Libraries -- Asynchronous Teams.
520	$aMetaheuristics, in their original definition, are solution methods that orchestrate an interaction between local improvement procedures and higher level strategies to create a process capable of escaping from local optima and performing a robust search of a solution space. Over time, these methods have also come to include any procedures that employ strategies for overcoming the trap of local optimality in complex solution spaces, especially those procedures that utilize one or more neighborhood structures as a means of defining admissible moves to transition from one solution to another, or to build or destroy solutions in constructive and destructive processes. The degree to which neighborhoods are exploited varies according to the type of procedure. In the case of certain population-based procedures, such as genetic al- rithms, neighborhoods are implicitly (and somewhat restrictively) defined by reference to replacing components of one solution with those of another, by variously chosen rules of exchange popularly given the name of “crossover. ” In other population-based methods, based on the notion of path relinking, neighborhood structures are used in their full generality, including constructive and destructive neighborhoods as well as those for transitioning between (complete) solutions. Certain hybrids of classical evoluti- ary approaches, which link them with local search, also use neighborhood structures more fully, though apart from the combination process itself.
538	$aOnline access to this digital book is restricted to subscription institutions through IP address (only for SISSA internal users)
700	$aGlover, Fred.$eeditor.
700	$aKochenberger, Gary A.$eeditor.
710	$aSpringerLink (Online service)
830	$aInternational Series in Operations Research & Management Science,$x0884-8289 ;$v57
856	$uhttp://dx.doi.org/10.1007/b101874