Advances in Evolutionary Algorithms

Advances in Evolutionary Algorithms

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.

Table of Contents

  • Limit Properties of Evolutionary Algorithms
  • Evolutionary Systems Identification: New Algorithmic Concepts and Applications
  • FPBIL: A Parameter-free Evolutionary Algorithm
  • A Memetic Algorithm Assisted by an Adaptive Topology RBF Network and Variable Local Models for Expensive Optimization Problems
  • An Adaptive Evolutionary Algorithm Combining Evolution Strategy and Genetic Algorithm (Application of Fuzzy Power System Stabilizer)
  • A Simple Hybrid Particle Swarm Optimization
  • Recent Advances in Harmony Search
  • A Hybrid Evolutionary Algorithm and its Application to Parameter Identification of Rolling Elements Bearings
  • Domain Decomposition Evolutionary Algorithm for Multi-Modal Function Optimization
  • Evolutionary Algorithms with Dissortative Mating on Static and Dynamic Environments
  • Adapting Genetic Algorithms for Combinatorial Optimization Problems in Dynamic Environments
  • Agent-Based Co-Evolutionary Techniques for Solving Multi-Objective Optimization Problems
  • Evolutionary Multi-Objective Robust Optimization
  • Improving Interpretability of Fuzzy Models Using Multi-Objective Neuro-Evolutionary Algorithms
  • Multi-objective Uniform-diversity Genetic Algorithm (MUGA)
  • EA-based Problem Solving Environment over the GRID
  • Evolutionary Methods for Learning Bayesian Network Structures
  • Design of Phased Antenna Arrays using Evolutionary Optimization Techniques
  • Design of an Efficient Genetic Algorithm to Solve the Industrial Car Sequencing Problem
  • Symbiotic Evolution Genetic Algorithms for Reinforcement Fuzzy Systems Design
  • Evolutionary Computation Applied to Urban Traffic Optimization
  • Evolutionary Algorithms in Decision Tree Induction

Book Details

Author(s): Witold Kosinski
Format(s): PDF
File size: 39.57 MB
Number of pages: 284
Link: Download.

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