Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Expanding from Discrete to Continuous Estimation of Distribution Algorithms: The IDEA
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
On the importance of diversity maintenance in estimation of distribution algorithms
GECCO '05 Proceedings of the 7th 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
SDR: a better trigger for adaptive variance scaling in normal EDAs
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Convergence phases, variance trajectories, and runtime analysis of continuous EDAs
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Truncation selection and Gaussian EDA: bounds for sustainable progress in high-dimensional spaces
Evo'08 Proceedings of the 2008 conference on Applications of evolutionary computing
When do heavy-tail distributions help?
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
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
Stochastic Local Search Techniques with Unimodal Continuous Distributions: A Survey
EvoWorkshops '09 Proceedings of the EvoWorkshops 2009 on Applications of Evolutionary Computing: EvoCOMNET, EvoENVIRONMENT, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, EvoNUM, EvoSTOC, EvoTRANSLOG
Estimation of distribution algorithms: from available implementations to potential developments
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Introducing intervention targeting into estimation of distribution algorithms
Proceedings of the 27th Annual ACM Symposium on Applied Computing
A review on probabilistic graphical models in evolutionary computation
Journal of Heuristics
Experimental comparison of six population-based algorithms for continuous black box optimization
Evolutionary Computation
Regularized continuous estimation of distribution algorithms
Applied Soft Computing
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When a simple real-valued estimation of distribution algorithm (EDA) with Gaussian model and maximum likelihood estimation of parameters is used, it converges prematurely even on the slope of the fitness function. The simplest way of preventing premature convergence by multiplying the variance estimate by a constant factor keach generation is studied. Recent works have shown that when increasing the dimensionality of the search space, such an algorithm becomes very quickly unable to traverse the slope and focus to the optimum at the same time. In this paper it is shown that when isotropic distributions with Gaussian or Cauchy distributed norms are used, the simple constant setting of kis able to ensure a reasonable behaviour of the EDA on the slope and in the valley of the fitness function at the same time.