Reactive Search Optimization | Optimization, Local Search, Heuristic, Tabu Search, Simulated Annealing, Global Optimization, Genetic Algorithm, Maxima and Minima, Function of a Real Variable

Lambert M. Surhone (u. a.)

ISBN 10: 6130345917 / ISBN 13: 9786130345914

Published by OmniScriptum, 2026

About this Item

Reactive Search Optimization | Optimization, Local Search, Heuristic, Tabu Search, Simulated Annealing, Global Optimization, Genetic Algorithm, Maxima and Minima, Function of a Real Variable | Lambert M. Surhone (u. a.) | Taschenbuch | Englisch | 2026 | OmniScriptum | EAN 9786130345914 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu Print on Demand.

Seller Inventory # 101376890

Bibliographic Details

• Title: Reactive Search Optimization | Optimization, Local Search, Heuristic, Tabu Search, Simulated Annealing, Global Optimization, Genetic Algorithm, Maxima and Minima, Function of a Real Variable
• Publisher: OmniScriptum
• Publication Date: 2026
• Language: English
• ISBN 10: 6130345917
• ISBN 13: 9786130345914
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 5D millimeters 220 millimeters width by 150 millimeters height by 5 millimeters depth
• Weight: 119 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. Reactive search optimization (RSO) is the common name for a family of optimization algorithms based on the local search techniques. It refers to a class of heuristics that automatically adjust their working parameters during the optimization phase. Reactive search optimization (RSO), like all local search techniques, is applied to the problem of finding the optimal configuration of a system; such configuration is usually composed of continuously or discretely varying parameters, while the optimality criterion is a numerical value associated to each configuration. In most cases, an optimization problem can be reduced to finding the (global) minimum of a function whose arguments are the configuration parameters, seen as free variables in the function''s domain space.