Read more
Zusatztext "...discusses two multi-objective optimization procedures! namely the ideal procedure and the preference-based one." (Zentralblatt MATH! Vol. 970! 2001/20) Informationen zum Autor Kalyanmoy Deb is an Indian computer scientist. Since 2013, Deb has held the Herman E. & Ruth J. Koenig Endowed Chair in the Department of Electrical and Computing Engineering at Michigan State University, which was established in 2001. Klappentext Evolutionary algorithms are very powerful techniques used to find solutions to real-world search and optimization problems. Many of these problems have multiple objectives, which leads to the need to obtain a set of optimal solutions, known as effective solutions. It has been found that using evolutionary algorithms is a highly effective way of finding multiple effective solutions in a single simulation run. Comprehensive coverage of this growing area of research Carefully introduces each algorithm with examples and in-depth discussion Includes many applications to real-world problems, including engineering design anf scheduling Accessible to those with limited knowledge of multi-objective optimization and evolutionary algorithms This integrated presentation of theory, algorithms and examples will benefit those working and researching in the areas of optimization, optimal design anf evolutionary computing. "Deb's book is complete, eminently readable, and the coverage is scholarly and thorough. It is my pleasure and duty to urge you to buy this book, read it, use it and enjoy it." - David E. Goldberg , University of Illinois at Urbana-Champaign, USA Zusammenfassung Evolutionary algorithms are relatively new, powerful techniques used to find solutions to many real--world search and optimization problems. Focusing on these "thinking" algorithms, this book offers comprehensive coverage of these techniques, which are highly effective in finding multiple effective solutions in a single simulation run. Inhaltsverzeichnis Foreword xv Preface xvii 1 Prologue 1 2 Multi-Objective Optimization 13 3 Classical Methods 49 4 Evolutionary Algorithms 81 5 Non-Elitist Multi-Objective Evolutionary Algorithms 171 6 Elitist Multi-Objective Evolutionary Algorithms 239 7 Constrained Multi-Objective Evolutionary Algorithms 289 8 Salient Issues of Multi-Objective Evolutionary Algorithms 315 9 Applications of Multi-Objective Evolutionary Algorithms 447 10 Epilogue 481 References 489 Index 509 ...
