Advances in Evolutionary Algorithms

486 Pages·2008·47.371 MB·English

by  Witold Kosinski (ed.)

Advances in Evolutionary Algorithms

Edited by Witold Kosinski
Advances in Evolutionary Algorithms

Edited by Witold Kosinski

D3pZ4i & bhgvld, Dennixxx & rosea (for softarchive)
DOI: 10.5772/73

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Published by AvE4EvA
Copyright © 2008
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ISBN-10 953-7619-11-7
ISBN-13 978-953-7619-11-4

C ontents

Preface

Part I: Foundations and New Methods
1 Limit Properties of Evolutionary Algorithms
2 Evolutionary Systems Identification: New Algorithmic Concepts

and Applications
3 FPBIL: A Parameter-free E volutionary Algorithm

Part II: Hybrid and Harmony Search
5 An Adaptive Evolutionary Algorithm Combining Evolution Strategy
and Genetic Algorithm (Application of Fuzzy Power System Stabilizer)

6 A Simple Hybrid Particle Swarm Optimization
7 Pxecent Advances in Harmony Search
A Hybrid Evolutionary Algorithm and its Application to Parameter
Identification of Rolling Elements Bearings
9 Domain Decomposition Evolutionary Algorithm for Multi-Modal
Function Optimization

Part III: Dynamic Environment and Multi-Objective Optimization
10 Evolutionary A lgorithms with Dissortative Mating on Static
and Dynamic Environments

11 Adapting Genetic Algorithms for Combinatorial Optimization
Problems in Dy namic Environments
12 Agent-Based Co-Evolutionary Techniques for Solving Multi-Objective
Optimization Problems
13 Evolutionary Multi-Objective PxObust Optimization

14 Improving InterpretabiIity of Fuzzy Models Using Multi-Objective
Neuro-Evolution ary Algorithms
15 Multi-objective Uniform-divers ity Genetic Algorithm (MUG A)

16 EA-based Problem Solving Environment over the GRID
VI Contents

Part IV: Applications
17 Evolutionary Methods for Learning Bayesian Network Structures

18 Design of Phased Antenna Arrays using Evolutionary Optimization
Techniques

19 Design of an Efficient Genetic Algorithm to Solve the Industrial
C ar Sequencing Problem
20 Symbiotic Evolution Genetic Algorithms for Reinforcement Fuzzy
Systems Design
21 Evolutionary Computation Applied to Urban Traffic Optimization
22 Evolutionary Algorithms in Decision Tree Induction

Preface

With the recent trends towards massive data sets and significant
computational power, combined with evolutionary algorithmic
advances evolutionary computation is becoming much more relevant
to practice.
Aim of the book is to present recent improvements, innovative
ideas and concepts in a part of a huge EA field.