On the importance of diversity maintenance in estimation of distribution algorithms

Yuan, Bo; Gallagher, Marcus

doi:10.1145/1068009.1068129

Cited by 63 publications

(49 citation statements)

References 8 publications

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“…Good results are reported for several problems in the literature whereas a high computational cost associated to the model induction stage is imposed in this class of EDAs. Finding a factorization can be a computationally expensive process and the resulting graph is often a suboptimal solution [4] [5].…”

Section: Introductionmentioning

confidence: 99%

Effective Linkage Learning Using Low-Order Statistics and Clustering

Emmendorfer

Pozo²

2009

IEEE Trans. Evol. Computat.

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Section: Introductionmentioning

confidence: 99%

Effective Linkage Learning Using Low-Order Statistics and Clustering

Emmendorfer

Pozo²

2009

IEEE Trans. Evol. Computat.

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“…It was recognized by many authors (see e.g. [3], [6], [7]) that such a learning scheme makes the algorithm very prone to premature convergence: in [8], it was shown also theoretically that the distance that can be traversed by a simple Gaussian EDA with truncation selection is bounded, and [9] showed similar results for tournament selection.…”

Section: Introductionmentioning

confidence: 99%

“…In [6], the variance is kept on values greater than 1, while [7] used self-adaptation of the variance. Adaptive variance scaling (AVS), i.e.…”

Section: Introductionmentioning

confidence: 99%

Truncation Selection and Gaussian EDA: Bounds for Sustainable Progress in High-Dimensional Spaces

Pošík

2008

Lecture Notes in Computer Science

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Abstract. In real-valued estimation-of-distribution algorithms, the Gaussian distribution is often used along with maximum likelihood (ML) estimation of its parameters. Such a process is highly prone to premature convergence. The simplest method for preventing premature convergence of Gaussian distribution is enlarging the maximum likelihood estimate of σ by a constant factor k each generation. Such a factor should be large enough to prevent convergence on slopes of the fitness function, but should not be too large to allow the algorithm converge in the neighborhood of the optimum. Previous work showed that for truncation selection such admissible k exists in 1D case. In this article it is shown experimentaly, that for the Gaussian EDA with truncation selection in high-dimensional spaces no admissible k exists!

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“…This is a point that has already been made, and some proposals for addressing the issue have been laid out [23,24,25,26,27]. This loss of diversity can be traced back to the above outliers issue of model-building algorithms.…”

Section: The Model-building Issuementioning

confidence: 99%

Multi-Objective Optimization with an Adaptive Resonance Theory-Based Estimation of Distribution Algorithm: A Comparative Study

Martí

García

Berlanga

et al. 2011

Lecture Notes in Computer Science

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Abstract. The introduction of learning to the search mechanisms of optimization algorithms has been nominated as one of the viable approaches when dealing with complex optimization problems, in particular with multi-objective ones. One of the forms of carrying out this hybridization process is by using multi-objective optimization estimation of distribution algorithms (MOEDAs). However, it has been pointed out that current MOEDAs have a intrinsic shortcoming in their model-building algorithms that hamper their performance.In this work we argue that error-based learning, the class of learning most commonly used in MOEDAs is responsible for current MOEDA underachievement. We present adaptive resonance theory (ART) as a suitable learning paradigm alternative and present a novel algorithm called multi-objective ART-based EDA (MARTEDA) that uses a Gaussian ART neural network for model-building and an hypervolume-based selector as described for the HypE algorithm. In order to assert the improvement obtained by combining two cutting-edge approaches to optimization an extensive set of experiments are carried out. These experiments also test the scalability of MARTEDA as the number of objective functions increases.

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On the importance of diversity maintenance in estimation of distribution algorithms

Cited by 63 publications

References 8 publications

Effective Linkage Learning Using Low-Order Statistics and Clustering

Effective Linkage Learning Using Low-Order Statistics and Clustering

Truncation Selection and Gaussian EDA: Bounds for Sustainable Progress in High-Dimensional Spaces

Multi-Objective Optimization with an Adaptive Resonance Theory-Based Estimation of Distribution Algorithm: A Comparative Study

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