Introduction.- Fundamentals of Search, Optimization and Decision Making.- Multiobjective EA Basics.- Multiobjective Cybernetics - Does Nature Solve Problems and Are They Multiobjective?.- Modularity Causes Multiple Objectives in Natural and Computational Systems.- Problem Decomposition, Modularity and their relation to Multiple Objectives.- Spatial Predator-Prey Models of Multiobjective Optimization.- Solution Concepts in Co-evolution and Multi-objective Search.- How Multiple Objectives are Used and their Effects on Solution Selection Methods.- Ill-Defined Problem Spaces.- Constrained Optimization via MOEAs.- Multiobjectivization.- Helper Objectives.- Learning Evaluation Functions for Global Optimization.- Assessing the Intrinsic Number of Objectives.- Assessing the Intrinsic Number of Decision Variables.- Fuzzy Dominance, Favour and other Relations beyond Pareto Optimality.- Multiobjective Clustering.- Reducing Bloat in GP with Multiple Objectives.- Multiobjective GP for Human-Understandable Models.- Multiobjective Supervised Learning.- Multiobjective Association Rule Mining.- Protein-folding via MOEAs and Solution Selection.- Unveiling Salient Insights in Engineering Designs with MOEAs.- Conclusions.- MOEA: Triumph of Natural Computing.- References.- Index

This text examines how multiobjective evolutionary algorithms and related techniques can be used to solve problems, particularly in the disciplines of science and engineering. Contributions by leading researchers show how the concept of multiobjective optimization can be used to reformulate and resolve problems in areas such as constrained optimization, co-evolution, classification, inverse modeling, and design.