This edited volume is targeted at presenting the latest state-of-the-art methodologies in "Hybrid Evolutionary Algorithms". The chapters deal with the theoretical and methodological aspects, as well as various applications to many real world problems from science, technology, business or commerce. Overall, the book has 14 chapters including an introductory chapter giving the fundamental definitions and some important research challenges. The contributions were selected on the basis of fundamental ideas/concepts rather than the thoroughness of techniques deployed.
Hybrid Evolutionary Algorithms: Methodologies, Architectures, and Reviews.- Quantum-Inspired Evolutionary Algorithm for Numerical Optimization.- Enhanced Evolutionary Algorithms for Multidisciplinary Design Optimization: A Control Engineering Perspective.- Hybrid Evolutionary Algorithms and Clustering Search.- A Novel Hybrid Algorithm for Function Optimization: Particle Swarm Assisted Incremental Evolution Strategy.- An Efficient Nearest Neighbor Classifier.- Hybrid Genetic: Particle Swarm Optimization Algorithm.- A Hybrid Genetic Algorithm and Bacterial Foraging Approach for Global Optimization and Robust Tuning of PID Controller with Disturbance Rejection.- Memetic Algorithms Parametric Optimization for Microlithography.- Significance of Hybrid Evolutionary Computation for Ab Initio Protein Folding Prediction.- A Hybrid Evolutionary Heuristic for Job Scheduling on Computational Grids.- Clustering Gene-Expression Data: A Hybrid Approach that Iterates Between k-Means and Evolutionary Search.- Robust Parametric Image Registration.- Pareto Evolutionary Algorithm Hybridized with Local Search for Biobjective TSP.