Multiobjective Optimization Using Evolutionary Algorithms - A Comparative Case Study
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Learning probability distributions in continuous evolutionary algorithms– a comparative review
Natural Computing: an international journal
Completely Derandomized Self-Adaptation in Evolution Strategies
Evolutionary Computation
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
A computational efficient covariance matrix update and a (1+1)-CMA for evolution strategies
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Covariance Matrix Adaptation for Multi-objective Optimization
Evolutionary Computation
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
The Journal of Machine Learning Research
S-metric calculation by considering dominated hypervolume as klee's measure problem
Evolutionary Computation
PISA: a platform and programming language independent interface for search algorithms
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
Steady-state selection and efficient covariance matrix update in the multi-objective CMA-ES
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
A new analysis of the lebmeasure algorithm for calculating hypervolume
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Performance assessment of multiobjective optimizers: an analysis and review
IEEE Transactions on Evolutionary Computation
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
New uncertainty handling strategies in multi-objective evolutionary optimization
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part II
The logarithmic hypervolume indicator
Proceedings of the 11th workshop proceedings on Foundations of genetic algorithms
GECCO 2012 tutorial on evolutionary multiobjective optimization
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
A comparative study of efficient initialization methods for the k-means clustering algorithm
Expert Systems with Applications: An International Journal
GECCO 2013 tutorial on evolutionary multiobjective optimization
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
Speeding up many-objective optimization by Monte Carlo approximations
Artificial Intelligence
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The multi-objective covariance matrix adaptation evolution strategy (MO-CMA-ES) is an evolutionary algorithm for continuous vector-valued optimization. It combines indicator-based selection based on the contributing hypervolume with the efficient strategy parameter adaptation of the elitist covariance matrix adaptation evolution strategy (CMA-ES). Step sizes (i.e., mutation strengths) are adapted on individual-level using an improved implementation of the 1/5-th success rule. In the original MO-CMA-ES, a mutation is regarded as successful if the offspring ranks better than its parent in the elitist, rank-based selection procedure. In contrast, we propose to regard a mutation as successful if the offspring is selected into the next parental population. This criterion is easier to implement and reduces the computational complexity of the MO-CMA-ES, in particular of its steady-state variant. The new step size adaptation improves the performance of the MO-CMA-ES as shown empirically using a large set of benchmark functions. The new update scheme in general leads to larger step sizes and thereby counteracts premature convergence. The experiments comprise the first evaluation of the MO-CMA-ES for problems with more than two objectives.