Multiobjective optimization using evolutionary algorithms

Evolutionary algorithms are relatively new, but very powerful techniques used to find solutions to many 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 researchCarefully introduces each algorithm with examples and in-depth discussionIncludes many applications to real-world problems, including engineering design and schedulingIncludes discussion of advanced topics and future researchCan be used as a course text or for self-studyAccessible to those with limited knowledge of classical multi-objective optimization and evolutionary algorithms The integrated presentation of theory, algorithms and examples will benefit those working and researching in the areas of optimization, optimal design and evolutionary computing.

This text provides an excellent introduction to the use of evolutionary algorithms in multi-objective optimization, allowing use as a graduate course text or for self-study.