Learning probability distributions in continuous evolutionary algorithms– a comparative review
Natural Computing: an international journal
On the importance of diversity maintenance in estimation of distribution algorithms
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Rigorous runtime analysis of a (μ+1)ES for the sphere function
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
Rigorous runtime analysis of the (1+1) ES: 1/5-rule and ellipsoidal fitness landscapes
FOGA'05 Proceedings of the 8th international conference on Foundations of Genetic Algorithms
Designing EDAs by using the elitist convergent EDA concept and the boltzmann distribution
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Preventing Premature Convergence in a Simple EDA Via Global Step Size Setting
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Approximating the search distribution to the selection distribution in EDAs
Proceedings of the 11th 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
Information Sciences: an International Journal
A Boltzmann based estimation of distribution algorithm
Information Sciences: an International Journal
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Considering the available body of literature on continuous EDAs, one must state that many important questions are still unanswered, e.g.: How do continuous EDAs really work, and how can we increase their efficiency further? The first question must be answered on the basis of formal models, but despite some recent results, the majority of contributions to the field is experimental. The second questionshould be answered by exploiting the insights that have been gained from formal models. We contribute to the theoretical literature on continuous EDAs by focussing on a simple, yet important, question: How should the variances used tosample offspring from change over an EDA run? To answer this question, the convergence process is separated into three phases and it is shown that for each phase, a preferable strategy exists for setting the variances. It is highly likely that the use of variances that have been estimated with maximum likelihood is not optimal. Thus, variance modification policies are not just a nice add-on. In the light of our findings, they become an integral component of continuous EDAs, and they should consider the specific requirements of all phases of the optimization process.