A robust evolutionary framework for multi-objective optimization
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Many Objective Optimisation: Direct Objective Boundary Identification
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Introduction to Evolutionary Multiobjective Optimization
Multiobjective Optimization
Multiobjective Optimization
Creating Human Activity Recognition Systems Using Pareto-based Multiobjective Optimization
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
Optimal strategies of the iterated prisoner's dilemma problem for multiple conflicting objectives
IEEE Transactions on Evolutionary Computation
Closed-loop evolutionary multiobjective optimization
IEEE Computational Intelligence Magazine
Using evolutionary multiobjective techniques for imbalanced classification data
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part I
Guest editorial: special issue on preference-based multiobjective evolutionary algorithms
IEEE Transactions on Evolutionary Computation - Special issue on preference-based multiobjective evolutionary algorithms
Multimodal optimization using a bi-objective evolutionary algorithm
Evolutionary Computation
Quantum control experiments as a testbed for evolutionary multi-objective algorithms
Genetic Programming and Evolvable Machines
Advances in evolutionary multi-objective optimization
SSBSE'12 Proceedings of the 4th international conference on Search Based Software Engineering
Power law-based local search in differential evolution
International Journal of Computational Intelligence Studies
Scalar vs. vector approach to bi-objective resource allocation in spatially distributed networks
International Journal of Innovative Computing and Applications
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Multiobjective problems involve several competing measures of solution quality, and multiobjective evolutionary algorithms (MOEAs) and multiobjective problem solving have become important topics of research in the evolutionary computation community over the past 10 years. This is an advanced text aimed at researchers and practitioners in the area of search and optimization. The book focuses on how MOEAs and related techniques can be used to solve problems, particularly in the disciplines of science and engineering. Contributions by leading researchers show how the concepts of multiobjective optimization can be used to reformulate and resolve problems in broad areas such as constrained optimization, coevolution, classification, inverse modelling and design. The book is distinguished from other texts on MOEAs in that it is not primarily about the algorithms, nor specific applications, but about the concepts and processes involved in solving problems using a multiobjective approach. Each chapter contributes to the central, deep concepts and themes of the book: evaluating the utility of the multiobjective approach; discussing alternative problem formulations; showing how problem formulation affects the search process; and examining solution selection and decision making. The book will be of benefit to researchers, practitioners and graduate students engaged with optimization-based problem solving. For multiobjective optimization experts, the book is an up-to-date account of emerging and advanced topics; for others, the book indicates how the multiobjective approach can lead to fresh insights.