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“Advances in Evolutionary Algorithms” ed. by Witold Kosinski

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"Advances in Evolutionary Algorithms" ed. by Witold Kosinski
InTeOpP | 2008 | ISBN: 9537619117 9789537619114 | 4786 pages | PDF | 47 MB
Aim of the book is to present recent improvements, innovative ideas and concepts in a part of a huge Evolutionary Algorithms field.

“Advances in Evolutionary Algorithms” ed. by Witold Kosinski
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.

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