Elements of information theory
Elements of information theory
Statistical machine learning and combinatorial optimization
Theoretical aspects of evolutionary computing
Numerical Optimization of Computer Models
Numerical Optimization of Computer Models
Extending Population-Based Incremental Learning to Continuous Search Spaces
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Information Sciences: an International Journal - Special issue: Evolutionary computation
On the importance of diversity maintenance in estimation of distribution algorithms
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Completely Derandomized Self-Adaptation in Evolution Strategies
Evolutionary Computation
The Estimation of Distributions and the Minimum Relative Entropy Principle
Evolutionary Computation
Population-Based Continuous Optimization, Probabilistic Modelling and Mean Shift
Evolutionary Computation
Probabilistic modeling for continuous EDA with Boltzmann selection and Kullback-Leibeler divergence
Proceedings of the 8th annual conference on Genetic and evolutionary computation
The correlation-triggered adaptive variance scaling IDEA
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)
The equation for response to selection and its use for prediction
Evolutionary Computation
Enhancing the Performance of Maximum---Likelihood Gaussian EDAs Using Anticipated Mean Shift
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
On the Parallel Speed-Up of Estimation of Multivariate Normal Algorithm and Evolution Strategies
EvoWorkshops '09 Proceedings of the EvoWorkshops 2009 on Applications of Evolutionary Computing: EvoCOMNET, EvoENVIRONMENT, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, EvoNUM, EvoSTOC, EvoTRANSLOG
Why one must use reweighting in estimation of distribution algorithms
Proceedings of the 11th 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
A memory efficient and continuous-valued compact EDA for large scale problems
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Benchmarking parameter-free amalgam on functions with and without noise
Evolutionary Computation
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This paper deals with the adaptive variance scaling issue incontinuous Estimation of Distribution Algorithms. A phenomenon is discovered that current adaptive variance scaling method in EDA suffers from imprecise structure learning. A new type of adaptation method is proposed to overcome this defect. The method tries to measure the difference between the obtained population and the prediction of the probabilistic model, then calculate the scaling factor by minimizing the cross entropy between these two distributions. This approach calculates the scaling factor immediately rather than adapts it incrementally. Experiments show that this approach extended the class of problems that can be solved, and improve the search efficiency in some cases. Moreover, the proposed approach features in that each decomposed subspace can be assigned an individual scaling factor, which helps to solve problems with special dimension property.