Scatter Search [electronic resource] : Methodology and Implementations in C /

The book Scatter Search by Manuel Laguna and Rafael Mart! 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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Main Authors: Laguna, Manuel. editor., Martí, Rafael. editor., SpringerLink (Online service)
Format: Texto biblioteca
Language:eng
Published: New York, NY : Springer US : Imprint: Springer, 2003
Subjects:Mathematics., Artificial intelligence., Mathematical optimization., Calculus of variations., Operations research., Management science., Optimization., Artificial Intelligence (incl. Robotics)., Operations Research, Management Science., Calculus of Variations and Optimal Control; Optimization.,
Online Access:http://dx.doi.org/10.1007/978-1-4615-0337-8
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id KOHA-OAI-TEST:178927
record_format koha
institution COLPOS
collection Koha
country México
countrycode MX
component Bibliográfico
access En linea
En linea
databasecode cat-colpos
tag biblioteca
region America del Norte
libraryname Departamento de documentación y biblioteca de COLPOS
language eng
topic Mathematics.
Artificial intelligence.
Mathematical optimization.
Calculus of variations.
Operations research.
Management science.
Mathematics.
Optimization.
Artificial Intelligence (incl. Robotics).
Operations Research, Management Science.
Calculus of Variations and Optimal Control; Optimization.
Mathematics.
Artificial intelligence.
Mathematical optimization.
Calculus of variations.
Operations research.
Management science.
Mathematics.
Optimization.
Artificial Intelligence (incl. Robotics).
Operations Research, Management Science.
Calculus of Variations and Optimal Control; Optimization.
spellingShingle Mathematics.
Artificial intelligence.
Mathematical optimization.
Calculus of variations.
Operations research.
Management science.
Mathematics.
Optimization.
Artificial Intelligence (incl. Robotics).
Operations Research, Management Science.
Calculus of Variations and Optimal Control; Optimization.
Mathematics.
Artificial intelligence.
Mathematical optimization.
Calculus of variations.
Operations research.
Management science.
Mathematics.
Optimization.
Artificial Intelligence (incl. Robotics).
Operations Research, Management Science.
Calculus of Variations and Optimal Control; Optimization.
Laguna, Manuel. editor.
Martí, Rafael. editor.
SpringerLink (Online service)
Scatter Search [electronic resource] : Methodology and Implementations in C /
description The book Scatter Search by Manuel Laguna and Rafael Mart! 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.
format Texto
topic_facet Mathematics.
Artificial intelligence.
Mathematical optimization.
Calculus of variations.
Operations research.
Management science.
Mathematics.
Optimization.
Artificial Intelligence (incl. Robotics).
Operations Research, Management Science.
Calculus of Variations and Optimal Control; Optimization.
author Laguna, Manuel. editor.
Martí, Rafael. editor.
SpringerLink (Online service)
author_facet Laguna, Manuel. editor.
Martí, Rafael. editor.
SpringerLink (Online service)
author_sort Laguna, Manuel. editor.
title Scatter Search [electronic resource] : Methodology and Implementations in C /
title_short Scatter Search [electronic resource] : Methodology and Implementations in C /
title_full Scatter Search [electronic resource] : Methodology and Implementations in C /
title_fullStr Scatter Search [electronic resource] : Methodology and Implementations in C /
title_full_unstemmed Scatter Search [electronic resource] : Methodology and Implementations in C /
title_sort scatter search [electronic resource] : methodology and implementations in c /
publisher New York, NY : Springer US : Imprint: Springer,
publishDate 2003
url http://dx.doi.org/10.1007/978-1-4615-0337-8
work_keys_str_mv AT lagunamanueleditor scattersearchelectronicresourcemethodologyandimplementationsinc
AT martirafaeleditor scattersearchelectronicresourcemethodologyandimplementationsinc
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spelling KOHA-OAI-TEST:1789272018-07-30T22:58:31ZScatter Search [electronic resource] : Methodology and Implementations in C / Laguna, Manuel. editor. Martí, Rafael. editor. SpringerLink (Online service) textNew York, NY : Springer US : Imprint: Springer,2003.engThe book Scatter Search by Manuel Laguna and Rafael Mart! 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.1. Introduction -- 1. Historical Background -- 2. Basic Design -- 3. C Code Conventions -- 2. Tutorial:Unconstrained Nonlinear Optimization -- 1. Diversification Generation Method -- 2. Improvement Method -- 3. Reference Set Update Method -- 4. Subset Generation Method -- 5. Combination Method -- 6. Overall Procedure -- 7. Summary of C Functions -- 3. Tutorial:0-1 Knapsack Problems -- 1. Diversification Generation Method -- 2. Improvement Method -- 3. Reference Set Update Method -- 4. Subset Generation Method -- 5. Combination Method -- 6. Overall Procedure -- 7. Summary of C Functions -- 4. Tutorial:Linear Ordering Problem -- 1. The Linear Ordering Problem -- 2. Diversification Generation Method -- 3. Improvement Method -- 4. Reference Set Update Method -- 5. Combination Method -- 6. Summary of C Functions -- 5. Advanced Scatter Search Designs -- 1. Reference Set -- 2. Subset Generation -- 3. Specialized Combination Methods -- 4. Diversification Generation -- 6. Use of Memory in Scatter Search -- 1. Tabu Search -- 2. Explicit Memory -- 3. Attributive Memory -- 7. Connections with Other Population-Based Approaches -- 1. Genetic Algorithms -- 2. Path Relinking -- 3. Intensification and Diversification -- 8. Scatter Search Applications -- 1. Neural Network Training -- 2. Multi-Objective Bus Routing -- 3. Arc Crossing Minimization in Graphs -- 4. Maximum Clique -- 5. Graph Coloring -- 6. Periodic Vehicle Loading -- 7. Capacitated Multicommodity Network Design -- 8. Job-Shop Scheduling -- 9. Capacitated Chinese Postman Problem -- 10. Vehicle Routing -- 11. Binary Mixed Integer Programming -- 12. Iterated Re-start Procedures -- 13. Parallelization for the P-Median -- 14. OptQuest Application -- 9. Commercial Scatter Search Implementation -- 1. General OCL Design -- 2. Constraints and Requirements -- 3. OCL Functionality -- 4. Computational Experiments -- 5. Conclusions -- 6. Appendix -- 10. Experiences and Future Directions -- 1. Experiences and Findings -- 2. Multi-Objective Scatter Search -- 3. Maximum Diversity Problem -- 4. Implications for Future Developments -- References.The book Scatter Search by Manuel Laguna and Rafael Mart! 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.Mathematics.Artificial intelligence.Mathematical optimization.Calculus of variations.Operations research.Management science.Mathematics.Optimization.Artificial Intelligence (incl. Robotics).Operations Research, Management Science.Calculus of Variations and Optimal Control; Optimization.Springer eBookshttp://dx.doi.org/10.1007/978-1-4615-0337-8URN:ISBN:9781461503378