Short description:

Evolutionary multiobjective optimization is currently gaining a lot of attention, particularly for researchers in the evolutionary computation communities. This monograph is suitable as a secondary text for graduate level computational intelligence courses, and as a reference for researchers, lecturers, and practitioners in industry.

Long description:

Multiobjective Evolutionary Algorithms and Applications provides comprehensive treatment on the design of multiobjective evolutionary algorithms and their applications in domains covering areas such as control and scheduling. Emphasizing both the theoretical developments and the practical implementation of multiobjective evolutionary algorithms, a profound mathematical knowledge is not required.

Written for a wide readership, engineers, researchers, senior undergraduates and graduate students interested in the field of evolutionary algorithms and multiobjective optimization with some basic knowledge of evolutionary computation will find this book a useful addition to their book case.

Table of Contents:

Review of MOEAs.- Conceptual Framework and Distribution Preservation Mechanisms for MOEAs.- Decision Supports and Advanced Features for MOEAs.- Dynamic Population Size and Local Exploration for MOEAs.- A Distributed Cooperative Coevolutionary Multiobjective Algorithm.- Learning the Search Range in Dynamic Environments.- Performance Assessment and Comparison of MOEAs.- A Multiobjective Evolutionary Algorithm Toolbox.- Evolutionary Computer-Aided Control System Design.- Evolutionary Design Automation of Multivariable QFT Control System.- Evolutionary Design of HDD Servo Control System.- Evolutionary Scheduling ? VRPTW.- Evolutionary Scheduling ? TTVRP.