Fuzzy Sets Based Heuristics for Optimization, Jos?-Luis Verdegay
Автор: Dietmar G. Maringer Название: Portfolio Management with Heuristic Optimization ISBN: 1441938427 ISBN-13(EAN): 9781441938428 Издательство: Springer Рейтинг: Цена: 23757.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The second part (Applications and Contributions) consists of five chapters, covering different problems in financial optimization: the effects of (linear, proportional and combined) transaction costs together with integer constraints and limitations on the initital endowment to be invested;
Описание: Constitutes the refereed proceedings of the International Workshop on Engineering Stochastic Local Search Algorithms 2007, held in Brussels, Belgium, September 6-8, 2007. This work covers topics including Methodological developments, behavior of SLS algorithms, search space analysis, algorithm performance, tuning procedures and AI/OR techniques.
Автор: Zbigniew Michalewicz; David B. Fogel Название: How to Solve It: Modern Heuristics ISBN: 3642061346 ISBN-13(EAN): 9783642061349 Издательство: Springer Рейтинг: Цена: 9083.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Автор: Ibrahim H. Osman; James P. Kelly Название: Meta-Heuristics ISBN: 1461285879 ISBN-13(EAN): 9781461285878 Издательство: Springer Рейтинг: Цена: 51570.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: These families of approaches include, but are not limited to greedy random adaptive search procedures, genetic algorithms, problem-space search, neural networks, simulated annealing, tabu search, threshold algorithms, and their hybrids.
Автор: Stefan Vo?; Silvano Martello; Ibrahim H. Osman; Ca Название: Meta-Heuristics ISBN: 1461376467 ISBN-13(EAN): 9781461376460 Издательство: Springer Рейтинг: Цена: 27950.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: 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).
Описание: Classical optimization methodologies fall short in very large and complex domains. In this book is suggested a different approach to optimization, an approach which is based on the 'blind' and heuristic mechanisms of evolution and population genetics. The genetic approach to optimization introduces a new philosophy to optimization in general, but particularly to engineering. By introducing the 'genetic' approach to robot trajectory generation, much can be learned about the adaptive mechanisms of evolution and how these mechanisms can solve real world problems. It is suggested further that optimization at large may benefit greatly from the adaptive optimization exhibited by natural systems when attempting to solve complex optimization problems, and that the determinism of classical optimization models may sometimes be an obstacle in nonlinear systems.This book is unique in that it reports in detail on an application of genetic algorithms to a real world problem, and explains the considerations taken during the development work. Futhermore, it addresses robotics in two new aspects: the optimization of the trajectory specification which has so far been done by human operators and has not received much attention for both automation and optimization, and the introduction of a heuristic strategy to a field predominated by deterministic strategies.Request Inspection Copy
Описание: Computer Science and Operations Research continue to have a synergistic relationship and this book - as a part of the Operations Research and Computer Science Interface Series - sits squarely in the center of the confluence of these two technical research communities.
Описание: This book presents recent advances on the design of intelligent systems based on fuzzy logic, neural networks and nature-inspired optimization and their application in areas such as, intelligent control and robotics, pattern recognition, time series prediction and optimization of complex problems.
Описание: This book presents recent advances on the design of intelligent systems based on fuzzy logic, neural networks and nature-inspired optimization and their application in areas such as, intelligent control and robotics, pattern recognition, time series prediction and optimization of complex problems.
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