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  1. Evolutionary algorithms for solving multi-objective problems [electronic resource]

    Coello Coello, Carlos A.
    2nd. ed. - Boston, MA : Springer Science+Business Media, LLC, 2007.

    This textbook is a second edition of Evolutionary Algorithms for Solving Multi-Objective Problems, significantly expanded and adapted for the classroom. The various features of multi-objective evolutionary algorithms are presented here in an innovative and student-friendly fashion, incorporating state-of-the-art research. The book disseminates the application of evolutionary algorithm techniques to a variety of practical problems. It contains exhaustive appendices, index and bibliography and links to a complete set of teaching tutorials, exercises and solutions.

    Online Ebook Central

  2. Evolutionary algorithms for solving multi-objective problems

    Coello Coello, Carlos A.
    2nd ed. - New York : Springer, ©2007.

    This textbook is the second edition of Evolutionary Algorithms for Solving Multi-Objective Problems, significantly augmented with contemporary knowledge and adapted for the classroom. All the various features of multi-objective evolutionary algorithms (MOEAs) are presented in an innovative and student-friendly fashion, incorporating state-of-the-art research results. The diversity of serial and parallel MOEA structures are given, evaluated and compared. The book provides detailed insight into the application of MOEA techniques to an array of practical problems. The assortment of test suites are discussed along with the variety of appropriate metrics and relevant statistical performance techniques. Distinctive features of the new edition include: Designed for graduate courses on Evolutionary Multi-Objective Optimization, with exercises and links to a complete set of teaching material including tutorials Updated and expanded MOEA exercises, discussion questions and research ideas at the end of each chapter New chapter devoted to coevolutionary and memetic MOEAs with added material on solving constrained multi-objective problems Additional material on the most recent MOEA test functions and performance measures, as well as on the latest developments on the theoretical foundations of MOEAs An exhaustive index and bibliography This self-contained reference is invaluable to students, researchers and in particular to computer scientists, operational research scientists and engineers working in evolutionary computation, genetic algorithms and artificial intelligence." ... If you still do not know this book, then, I urge you to run-don't walk-to your nearest on-line or off-line book purveyor and click, signal or otherwise buy this important addition to our literature."--David E. Goldberg, University of Illinois at Urbana-ChampaignThis textbook is a second edition of Evolutionary Algorithms for Solving Multi-Objective Problems, significantly expanded and adapted for the classroom. The various features of multi-objective evolutionary algorithms are presented here in an innovative and student-friendly fashion, incorporating state-of-the-art research. The book disseminates the application of evolutionary algorithm techniques to a variety of practical problems. It contains exhaustive appendices, index and bibliography and links to a complete set of teaching tutorials, exercises and solutions.

    Online SpringerLink

  3. MICAI 2002: Advances in Artificial Intelligence : Second Mexican International Conference on Artificial Intelligence Mérida, Yucatán, Mexico, April 22 26, 2002 Proceedings

    Coello Coello, Carlos A.
    Berlin ; Heidelberg : Springer-Verlag Berlin Heidelberg, 2002.

    This book constitutes the refereed proceedings of the Second Mexican International Conference on Artificial Intelligence, MICAI 2002, held in Mrida, Yucatn, Mexico in April 2002. The 56 revised full papers presented were carefully reviewed and selected from more than 85 submissions from 17 countries. The papers are organized in topical sections on robotics and computer vision, heuristic search and optimization, speech recognition and natural language processing, logic, neural networks, machine learning, multi-agent systems, uncertainty management, and AI tools and applications.

    Online SpringerLink

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