Nature-Inspired Algorithms for Optimisation

• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Άλλοι συγγραφείς: Chiong, Raymond (Επιμελητής έκδοσης)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Berlin, Heidelberg : Springer Berlin Heidelberg, 2009.
• Σειρά: Studies in Computational Intelligence, 193
• Θέματα: Engineering.; Operations research.; Decision making.; Artificial intelligence.; Applied mathematics.; Engineering mathematics.; Appl.Mathematics/Computational Methods of Engineering.; Artificial Intelligence (incl. Robotics).; Operation Research/Decision Theory.

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...

Πίνακας περιεχομένων:

• 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.