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...
保存先:
| 第一著者: | |
|---|---|
| その他の著者: | , |
| フォーマット: | Livre numérique |
| 言語: | Anglais |
| 出版事項: |
Berlin, Heidelberg :
Springer Berlin Heidelberg
[20..].
Cham : Springer Nature |
| 版: | 1st ed. 2009. |
| シリーズ: | Studies in Computational Intelligence
193 |
| オンライン･アクセス: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| 注記: |
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 |
目次:
- 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