Nature-Inspired algorithms for optimisation
Nature-Inspired Algorithms have been gaining much popularity in recent years due to the fact that many real-world optimisation problems have become increasingly large, complex and dynamic. The size and complexity of the problems nowadays require the development of methods and solutions whose efficie...
Tallennettuna:
| Päätekijä: | |
|---|---|
| Muut tekijät: | , |
| Aineistotyyppi: | Livre numérique |
| Kieli: | Anglais |
| Julkaistu: |
Berlin, Heidelberg :
Springer Berlin Heidelberg
[20..].
Cham : Springer Nature |
| Painos: | 1st ed. 2009. |
| Sarja: | Studies in Computational Intelligence
193 |
| Linkit: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Huomautus: |
Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Nature-Inspired Algorithms for Optimisation, Texte imprimé, 9783642002663 • Nature-Inspired Algorithms for Optimisation, Texte imprimé, 9783642003608 • Nature-Inspired Algorithms for Optimisation, Texte imprimé, 9783642101304 • Nature-Inspired Algorithms for Optimisation, Texte imprimé, 9783642002663 |
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| 100 | 1 | |a Chiong, Raymond. | |
| 245 | 1 | 0 | |a Nature-Inspired algorithms for optimisation |c edited by Janusz Kacprzyk, Raymond Chiong. |
| 250 | |a 1st ed. 2009. | ||
| 260 | |a Berlin, Heidelberg : |b Springer Berlin Heidelberg. | ||
| 260 | |a Cham : |b Springer Nature, |c [20..]. | ||
| 490 | 0 | |a Studies in Computational Intelligence |v 193 |x 1860-9503 | |
| 500 | |a Archives Springer e-books (Licence nationale) | ||
| 500 | |a Archives Springer e-books (Licence nationale) | ||
| 505 | 1 | |a Section I: Introduction Why Is Optimization Difficult? The Rationale Behind Seeking Inspiration from Nature Section II: Evolutionary Intelligence The Evolutionary-Gradient-Search Procedure in Theory and Practice The Evolutionary Transition Algorithm: Evolving Complex Solutions Out of Simpler Ones A Model-Assisted Memetic Algorithm for Expensive Optimization Problems A Self-adaptive Mixed Distribution Based Uni-variate Estimation of Distribution Algorithm for Large Scale Global Optimization Differential Evolution with Fitness Diversity Self-adaptation Central Pattern Generators: Optimisation and Application Section III: Collective Intelligence Fish School Search Magnifier Particle Swarm Optimization Improved Particle Swarm Optimization in Constrained Numerical Search Spaces Applying River Formation Dynamics to Solve NP-Complete Problems Section IV: Social-Natural Intelligence Algorithms Inspired in Social Phenomena Artificial Immune Systems for Optimization Section V: Multi-Objective Optimisation Ranking Methods in Many-Objective Evolutionary Algorithms On the Effect of Applying a Steady-State Selection Scheme in the Multi-Objective Genetic Algorithm NSGA-II Improving the Performance of Multiobjective Evolutionary Optimization Algorithms Using Coevolutionary Learning Evolutionary Optimization for Multiobjective Portfolio Selection under Markowitz s Model with Application to the Caracas Stock Exchange | |
| 506 | |a Accès en ligne pour les établissements français bénéficiaires des licences nationales | ||
| 506 | |a Accès soumis à abonnement pour tout autre établissement | ||
| 506 | |a Conditions particulières de réutilisation pour les bénéficiaires des licences nationales. https://www.licencesnationales.fr/springer-nature-ebooks-contrat-licence-ln-2017 | ||
| 520 | |a Nature-Inspired Algorithms have been gaining much popularity in recent years due to the fact that many real-world optimisation problems have become increasingly large, complex and dynamic. The size and complexity of the problems nowadays require the development of methods and solutions whose efficiency is measured by their ability to find acceptable results within a reasonable amount of time, rather than an ability to guarantee the optimal solution. This volume 'Nature-Inspired Algorithms for Optimisation' is a collection of the latest state-of-the-art algorithms and important studies for tackling various kinds of optimisation problems. It comprises 18 chapters, including two introductory chapters which address the fundamental issues that have made optimisation problems difficult to solve and explain the rationale for seeking inspiration from nature. The contributions stand out through their novelty and clarity of the algorithmic descriptions and analyses, and lead the way to interesting and varied new applications | ||
| 700 | 1 | |a Kacprzyk, Janusz, |d 1947- |4 pbd | |
| 700 | 1 | |a Chiong, Raymond, |d 19..- |4 pbd | |
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| 776 | 0 | |t Nature-Inspired Algorithms for Optimisation |b Texte imprimé |z 9783642003608 | |
| 776 | 0 | |t Nature-Inspired Algorithms for Optimisation |b Texte imprimé |z 9783642101304 | |
| 776 | 0 | |t Nature-Inspired Algorithms for Optimisation |b Texte imprimé |z 9783642002663 | |
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