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 comparedto its conventional counterparts. Nonetheless, researchers are only beginningto 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. Recent research on Multi-objective Memetic Algorithms INDICE: Part I Introduction Evolutionary Multi-Multi-Objective Optimization – EMMOO.- Part II Knowledge Infused in Design of Problem-Specific Operators Solving Time-Tabling Problems using Evolutionary Algorithms and Heuristics Search .- Part III Knowledge Propagation through Cultural Evolution Risk and CostTradeoff In Economic Dispatch Including Wind Power Penetration Based on Multi-objective Memetic Particle Swarm Optimization .- Part IV Information Exploited for Local Improvement Combination of Genetic Algorithms and Evolution Strategies with Self-Adaptive Switching.
- ISBN: 978-3-540-88050-9
- Editorial: Springer
- Encuadernacion: Cartoné
- Páginas: 410
- Fecha Publicación: 01/01/2009
- Nº Volúmenes: 1
- Idioma: Inglés