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Studies in Computational Intelligence

By: Raymond Chiong (Editor)

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Hardcover | 28 April 2009

At a Glance

Hardcover
536 Pages

Dimensions(cm)
24.13 x 16.51 x 3.18

Hardcover

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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.

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You Can Find This Book In

Non-Fiction Mathematics Algebra Optimisation Applied Mathematics Computing & I.T.Computer Science Artificial Intelligence Business & Management Operational Research Engineering & Technology Technology in General

Engineering in General Maths for Engineers Probability & Statistics Management & Management Techniques Management Decision Making

Introduction
Why Is Optimization Difficult?
The Rationale behind Seeking Inspiration from Nature
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
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
Social-Natural Intelligence
Algorithms Inspired in Social Phenomena
Artif
Table of Contents provided by Publisher. All Rights Reserved.

Studies in Computational Intelligence

At a Glance

Hardcover

Shipping

How to return your order

Defective items

You Can Find This Book In

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