Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Combining convergence and diversity in evolutionary multiobjective optimization
Evolutionary Computation
Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization
Proceedings of the 5th International Conference on Genetic Algorithms
Multiobjective Evolutionary Algorithms: Analyzing the State-of-the-Art
Evolutionary Computation
Reference point based multi-objective optimization using evolutionary algorithms
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Towards estimating nadir objective vector using evolutionary approaches
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Covariance Matrix Adaptation for Multi-objective Optimization
Evolutionary Computation
Do additional objectives make a problem harder?
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
An EMO algorithm using the hypervolume measure as selection criterion
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
Asymptotic convergence of some metaheuristics used for multiobjective optimization
FOGA'05 Proceedings of the 8th international conference on Foundations of Genetic Algorithms
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
Preferences and their application in evolutionary multiobjectiveoptimization
IEEE Transactions on Evolutionary Computation
Properties of an adaptive archiving algorithm for storing nondominated vectors
IEEE Transactions on Evolutionary Computation
Performance assessment of multiobjective optimizers: an analysis and review
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A grid-based fitness strategy for evolutionary many-objective optimization
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Set-based multi-objective optimization, indicators, and deteriorative cycles
Proceedings of the 12th annual conference on Genetic and evolutionary computation
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Defining and optimizing indicator-based diversity measures in multiobjective search
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
On expected-improvement criteria for model-based multi-objective optimization
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
SEAL'10 Proceedings of the 8th international conference on Simulated evolution and learning
The logarithmic hypervolume indicator
Proceedings of the 11th workshop proceedings on Foundations of genetic algorithms
Convergence of hypervolume-based archiving algorithms I: effectiveness
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Set-based multiobjective fitness landscapes: a preliminary study
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Evolution strategies and multi-objective optimization of permanent magnet motor
Applied Soft Computing
Hypervolume-based multiobjective optimization: Theoretical foundations and practical implications
Theoretical Computer Science
On the effect of connectedness for biobjective multiple and long path problems
LION'05 Proceedings of the 5th international conference on Learning and Intelligent Optimization
Diversity Guided Evolutionary Programming: A novel approach for continuous optimization
Applied Soft Computing
The fuzzy-genetic system for multiobjective optimization
SIDE'12 Proceedings of the 2012 international conference on Swarm and Evolutionary Computation
Convergence of hypervolume-based archiving algorithms ii: competitiveness
Proceedings of the 14th annual conference on Genetic and evolutionary computation
A new multi-objective evolutionary algorithm based on a performance assessment indicator
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Convergence of set-based multi-objective optimization, indicators and deteriorative cycles
Theoretical Computer Science
Recombination of similar parents in SMS-EMOA on many-objective 0/1 knapsack problems
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part II
Bounding the effectiveness of hypervolume-based (µ+λ)-archiving algorithms
LION'12 Proceedings of the 6th international conference on Learning and Intelligent Optimization
Approximation quality of the hypervolume indicator
Artificial Intelligence
On set-based local search for multiobjective combinatorial optimization
Proceedings of the 15th annual conference on Genetic and evolutionary computation
A Modified micro Genetic Algorithm for undertaking Multi-Objective Optimization Problems
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Recent Advances in Soft Computing: Theories and Applications
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Assuming that evolutionary multiobjective optimization (EMO) mainly deals with set problems, one can identify three core questions in this area of research: 1) how to formalize what type of Pareto set approximation is sought; 2) how to use this information within an algorithm to efficiently search for a good Pareto set approximation; and 3) how to compare the Pareto set approximations generated by different optimizers with respect to the formalized optimization goal. There is a vast amount of studies addressing these issues from different angles, but so far only a few studies can be found that consider all questions under one roof. This paper is an attempt to summarize recent developments in the EMO field within a unifying theory of set-based multiobjective search. It discusses how preference relations on sets can be formally defined, gives examples for selected user preferences, and proposes a general preferenceindependent hill climber for multiobjective optimization with theoretical convergence properties. Furthermore, it shows how to use set preference relations for statistical performance assessment and provides corresponding experimental results. The proposed methodology brings together preference articulation, algorithm design, and performance assessment under one framework and thereby opens up a new perspective on EMO.