Evolutionary Algorithms for Solving MultiObjective Problemsby Carlos A. Coello Coello, David A. Van Veldhuizen, Gary B. Lamont Volume 5 of the Book Series on Genetic Algorithms and Evolutionary Computation Kluwer Academic Publishers; ISBN: 0306467623, May 2002 

The solving of multiobjective problems (MOPs) has been a continuing effort by humans in many diverse areas including computer science, engineering, economics, finance, industry, physics, chemistry, and ecology, among others. Many powerful deterministic and stochastic techniques for solving these large dimensional optimization problems have risen out of operations research, decision science, engineering, computer science and other related disciplines. The explosion in computing power continues to arouse extraordinary interest in stochastic search algorithms that require high computational speed and very large memories. A generic stochastic approach is that of evolutionary algorithms (EAs). Such algorithms have been demonstrated to be very powerful and generally applicable for solving difficult single objective problems. Their fundamental algorithmic structures can also be applied to solving many multiobjective problems. In this book, the various features of multiobjective evolutionary algorithms (MOEAs) are presented in an innovative and unique fashion, with detailed customized forms suggested for a variety of applications. Also, extensive MOEA discussion questions and possible research directions are presented at the end of each chapter. Based upon the original contributions of Darwin and Mendel, evolution occurs through natural selection and adaptation. Using this basic biological model, various evolutionary algorithm structures have been developed. Single objective EAs and in particular genetic algorithms (GAs), evolutionary programming (EP) and evolution strategies (ES) have been shown to find if not the optimal solution something that is satisfactory; i.e. "satisfices'' the user. The goal of course is to search the associated objective/fitness function landscape (phenotype space) through exploration and exploitation for the "optimal'' solution. Such activity is controlled through the use of biologically inspired "mating'', "mutation'' and "selection'' operators. Specific evolutionary algorithm development involves the encoding of the independent variables (genotype) and the structuring of specific parametric mating, mutation, and selection operators. These operators manipulate each genotype individual appropriately as the search proceeds through the phenotype landscape. The design of innovative evolutionary algorithmic approaches for multiobjective problems is built upon the research and development for single objective functions. Understanding this body of knowledge lends insight to the design and implementation of MOEAs. The use of MOEAs requires insight not only of the algorithmic domain, but also knowledge of the application problem domain. This monograph addresses such variations in the development of multiobjective evolutionary algorithms (MOEA), associated theory, appropriate multiobjective problems (MOPs) for MOEA testing, and experience with realworld applications. Many references are included and suggested for further reading. Applying the fundamental concepts of MOEAs to realworld problems was initially a curiosity, but today is a common trend. By using the concepts and techniques presented in this book one can obtain insight into the selection of an MOEA software platform and associated tuning of the various operator parameters for complex applications. Moreover, most complex realworld applications have side constraints which requires MOEA tailoring in searching the fitness landscape. This book attempts to address all these issues through the following features:
The flow of material in each chapter is intended to present a natural and comprehensive development of MOEAs from basic concepts to complex applications. As previously stated, at the end of each chapter a list of possible research topics is given along with a number of pertinent discussion questions. 

You can order this book from Kluwer or Amazon, or you can download the order form from here. 
You can find here a couple of pictures of the presentation of this book that Dr. Coello organized in Mexico City. 



This book contains several discussion questions and research ideas. The first are meant to be used as exercises either in class or for assignments. The second are meant to trigger everything from a small class project all the way up to a PhD thesis project. Despite the information previously indicated, the evolutionary multiobjective optimization is constantly growing and therefore, we will be adding additional exercises inspired on current research. The following are additional exercises for each of the chapters of this book: 

1. BASIC CONCEPTS
2. EVOLUTIONARY ALGORITHM MOP APPROACHES
3. MOEA TEST SUITES
4. MOEA TESTING AND ANALYSIS
5. MOEA THEORY AND ISSUES
6. APPLICATIONS
7. MOEA PARALLELIZATION
8. MULTICRITERIA DECISION MAKING
9. SPECIAL TOPICS
10. EPILOG
Appendix A: MOEACLASSIFICATIONANDTECHNIQUEANALYSIS Appendix B: MOPs IN THE LITERATURE Appendix C: Ptrue & PFtrue FOR SELECTED NUMERIC MOPs Appendix D: Ptrue & PFtrue FOR SIDECONSTRAINED MOPs Appendix E: MOEA SOFTWARE AVAILABILITY Appendix F: MOEARELATED INFORMATION 
Volume 1: Efficient
and Accurate Parallel Genetic Algorithms by Erick
CantúPaz
Volume 2: Estimation
of Distribution Algorithms: A New Tool for Evolutionary Computation
by Pedro
Larrañaga, José
A. Lozano
Volume 3: Evolutionary
Optimization in Dynamic Environmentsby Jürgen
Branke
Volume 4: Anticipatory
Learning Classifier Systems by Martin
V. Butz
Volume 6: OmeGA
by Dimitri Knjazew
Volume 7: The Design of
Innovation by David
E. Goldberg
Volume 8: Noisy
Optimization with Evolution Strategies by Dirk
V. Arnold
Volume 9: Classical
and Evolutionary Algorithms in the Optimization of Optical Systems
by Darko
Vasiljevic
Volume 10: Evolutionary
Algorithms for Embedded System Design by Rolf
Drechsler and Nicole
Drechsler
Further information about this book series is available here