Multi-Objective Memetic Algorithms

Memetic algorithms are a success story in sophisticated evolutionary computing. Written for as wide a readership as possible, this book reflects the current state-of-the-art in the theory and practice of Memetic algorithms and is an invaluable reference.

The application of sophisticated evolutionary computing approaches for solving complex problems with multiple conflicting objectives in science and engineering have increased steadily in the recent years. Within this growing trend, Memetic algorithms are, perhaps, one of the most successful stories, having demonstrated better efficacy in dealing with multi-objective problems as compared to its conventional counterparts. Nonetheless, researchers are only beginning to realize the vast potential of multi-objective Memetic algorithm and there remain many open topics in its design.

This book presents a very first comprehensive collection of works, written by leading researchers in the field, and reflects the current state-of-the-art in the theory and practice of multi-objective Memetic algorithms. "Multi-Objective Memetic algorithms" is organized for a wide readership and will be a valuable reference for engineers, researchers, senior undergraduates and graduate students who are interested in the areas of Memetic algorithms and multi-objective optimization.

Recent research on Multi-objective Memetic Algorithms Includes supplementary material: sn.pub/extras

Inhalt
Evolutionary Multi-Multi-Objective Optimization - EMMOO.- Implementation of Multiobjective Memetic Algorithms for Combinatorial Optimization Problems: A Knapsack Problem Case Study.- Knowledge Infused in Design of Problem-Specific Operators.- Solving Time-Tabling Problems Using Evolutionary Algorithms and Heuristics Search.- An Efficient Genetic Algorithm with Uniform Crossover for the Multi-Objective Airport Gate Assignment Problem.- Application of Evolutionary Algorithms for Solving Multi-Objective Simulation Optimization Problems.- Feature Selection Using Single/Multi-Objective Memetic Frameworks.- Multi-Objective Robust Optimization Assisted by Response Surface Approximation and Visual Data-Mining.- Multiobjective MetamodelAssisted Memetic Algorithms.- A Convergence Acceleration Technique for Multiobjective Optimisation.- Knowledge Propagation through Cultural Evolution.- Risk and Cost Tradeoff in Economic Dispatch Including Wind Power Penetration Based on Multi-Objective Memetic Particle Swarm Optimization.- Hybrid Behavioral-Based Multiobjective Space Trajectory Optimization.- Nature-Inspired Particle Mechanics Algorithm for Multi-Objective Optimization.- Information Exploited for Local Improvement.- Combination of Genetic Algorithms and Evolution Strategies with Self-adaptive Switching.- Comparison between MOEA/D and NSGA-II on the Multi-Objective Travelling Salesman Problem.- Integrating Cross-Dominance Adaptation in Multi-Objective Memetic Algorithms.- A Memetic Algorithm for Dynamic Multiobjective Optimization.- A Memetic Coevolutionary Multi-Objective Differential Evolution Algorithm.- Multiobjective Memetic Algorithm and Its Application in Robust Airfoil Shape Optimization.