Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Combining convergence and diversity in evolutionary multiobjective optimization
Evolutionary Computation
Why Quality Assessment Of Multiobjective Optimizers Is Difficult
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Handbook of Mathematical Functions, With Formulas, Graphs, and Mathematical Tables,
Handbook of Mathematical Functions, With Formulas, Graphs, and Mathematical Tables,
Completely Derandomized Self-Adaptation in Evolution Strategies
Evolutionary Computation
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
The correlation-triggered adaptive variance scaling IDEA
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Combining gradient techniques for numerical multi-objective evolutionary optimization
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Towards a New Evolutionary Computation: Advances on Estimation of Distribution Algorithms (Studies in Fuzziness and Soft Computing)
Scalable Optimization via Probabilistic Modeling: From Algorithms to Applications (Studies in Computational Intelligence)
An overview of evolutionary algorithms in multiobjective optimization
Evolutionary Computation
Multi-objective genetic algorithms: Problem difficulties and construction of test problems
Evolutionary Computation
Scalability problems of simple genetic algorithms
Evolutionary Computation
Fda -a scalable evolutionary algorithm for the optimization of additively decomposed functions
Evolutionary Computation
The balance between proximity and diversity in multiobjective evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Evolutionary Multiobjective Optimization for Dynamic Hospital Resource Management
EMO '09 Proceedings of the 5th International Conference on Evolutionary Multi-Criterion Optimization
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Variance scaling for EDAs revisited
KI'11 Proceedings of the 34th Annual German conference on Advances in artificial intelligence
Proceedings of the 14th annual conference on Genetic and evolutionary computation
WCCI'12 Proceedings of the 2012 World Congress conference on Advances in Computational Intelligence
Regularized continuous estimation of distribution algorithms
Applied Soft Computing
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Recent research into single-objective continuous Estimation-of-Distribution Algorithms (EDAs)has shown that when maximum-likelihood estimationsare used for parametric distributions such as thenormal distribution, the EDA can easily suffer frompremature convergence. In this paper we argue thatthe same holds for multi-objective optimization.Our aim in this paper is to transfer a solutioncalled Adaptive Variance Scaling (AVS) from thesingle-objective case to the multi-objectivecase. To this end, we zoom in on an existing EDAfor continuous multi-objective optimization, theMIDEA, which employs mixturedistributions. We propose a means to combine AVSwith the normal mixture distribution, as opposedto the single normal distribution for which AVS wasintroduced. In addition, we improve the AVS schemeusing the Standard-Deviation Ratio(SDR) trigger. Intuitively put, variance scalingis triggered by the SDR trigger only ifimprovements are found to be far awayfrom the mean. For the multi-objective case,this addition is important to keep the variancefrom being scaled to excessively large values.From experiments performed on five well-knownbenchmark problems, the addition of SDR andAVS is found to enlarge the class of problems thatcontinuous multi-objective EDAs can solve reliably.