Evolutionary Algorithms for Solving Multi-Objective Problems

Solving multi-objective problems is an evolving effort, and computer science and other related disciplines have given rise to many powerful deterministic and stochastic techniques for addressing these large-dimensional optimization problems. Evolutionary algorithms are one such generic stochastic approach that has proven to be successful and widely applicable in solving both single-objective and multi-objective problems.

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, including test suites with associated performance based on a variety of appropriate metrics, as well as serial and parallel algorithm implementations.

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 purveyorand click, signal or otherwise buy this important addition to our literature."

-David E. Goldberg, University of Illinois at Urbana-Champaign