Nature-Inspired Algorithms for Optimisation
| Autor Corporativo: | |
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| Otros Autores: | |
| Formato: | eBook |
| Lenguaje: | English |
| Publicado: |
Berlin, Heidelberg :
Springer Berlin Heidelberg : Imprint: Springer,
2009.
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| Edición: | 1st ed. 2009. |
| Colección: | Studies in Computational Intelligence,
193 |
| Materias: | |
| Acceso en línea: | https://doi.org/10.1007/978-3-642-00267-0 |
Tabla de Contenidos:
- 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.