Evolutionary Multi Objective is an important area which is presently gaining considerable attention. There is a lack of books in the area.
The book will be recommended for the authors´ two computational intelligence courses (often with a total of over 150 students)
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.
Preface Introduction Review of MOEAs Conceptual Framework and Distribution Preservation Mechanisms for MOEAs Dynamic Population Size and Local Exploration for MOEAs A Distributed Cooperative Co-evolutionary 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 for Multivariable QFT Control System Evolutionary Design of a HDD Control System Evolutionary Scheduling - VRPTW Evolutionary Scheduling - TTVRP Bibliography Index
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.
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.
Inhaltsverzeichnis
Preface Introduction Review of MOEAs Conceptual Framework and Distribution Preservation Mechanisms for MOEAs Dynamic Population Size and Local Exploration for MOEAs A Distributed Cooperative Co-evolutionary 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 for Multivariable QFT Control System Evolutionary Design of a HDD Control System Evolutionary Scheduling - VRPTW Evolutionary Scheduling - TTVRP Bibliography Index
Klappentext
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.
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.
