Bicriterion Single Machine Scheduling with Resource Dependent Processing Times
SIAM Journal on Optimization
Combining convergence and diversity in evolutionary multiobjective optimization
Evolutionary Computation
Efficiently computing succinct trade-off curves
Theoretical Computer Science - Automata, languages and programming: Algorithms and complexity (ICALP-A 2004)
Convergence of stochastic search algorithms to finite size pareto set approximations
Journal of Global Optimization
Multiobjective immune algorithm with nondominated neighbor-based selection
Evolutionary Computation
Theory of the hypervolume indicator: optimal μ-distributions and the choice of the reference point
Proceedings of the tenth ACM SIGEVO workshop on Foundations of genetic algorithms
Multiplicative approximations and the hypervolume indicator
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Multiobjective optimization problems with complicated Pareto sets, MOEA/D and NSGA-II
IEEE Transactions on Evolutionary Computation
Computing gap free pareto front approximations with stochastic search algorithms
Evolutionary Computation
Approximating the volume of unions and intersections of high-dimensional geometric objects
Computational Geometry: Theory and Applications
How good is the Chord algorithm?
SODA '10 Proceedings of the twenty-first annual ACM-SIAM symposium on Discrete Algorithms
Small Approximate Pareto Sets for Biobjective Shortest Paths and Other Problems
SIAM Journal on Computing
An EMO algorithm using the hypervolume measure as selection criterion
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
A scalable multi-objective test problem toolkit
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Approximation-guided evolutionary multi-objective optimization
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
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Approximation-Guided Evolution (AGE) [4] is a recently presented multi-objective algorithm that outperforms state-of-the-art multi-multi-objective algorithms in terms of approximation quality. This holds for problems with many objectives, but AGE's performance is not competitive on problems with few objectives. Furthermore, AGE is storing all non-dominated points seen so far in an archive, which can have very detrimental effects on its runtime. In this article, we present the fast approximation-guided evolutionary algorithm called AGE-II. It approximates the archive in order to control its size and its influence on the runtime. This allows for trading-off approximation and runtime, and it enables a faster approximation process. Our experiments show that AGE-II performs very well for multi-objective problems having few as well as many objectives. It scales well with the number of objectives and enables practitioners to add objectives to their problems at small additional computational cost.