Advances in Evolutionary Algorithms

Cover
Advances in Evolutionary Algorithms
©
Contents
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 Evolutionary 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 Recent Advances in Harmony Search
	8 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 Algorithms with Dissortative Mating on Static and Dynamic Environments
	11 Adapting Genetic Algorithms for Combinatorial Optimization Problems in Dynamic Environments
	12 Agent-Based Co-Evolutionary Techniques for Solving Multi-Objective Optimization Problems
	13 Evolutionary Multi-Objective Robust Optimization
	14 Improving Interpretability of Fuzzy Models Using Multi-Objective Neuro-Evolutionary Algorithms
	15 Multi-objective Uniform-diversity Genetic Algorithm (MUGA)
	16 EA-based Problem Solving Environment over the GRID
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 Car 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