Dependency Structure Matrix, Genetic Algorithms, and Effective Recombination

Yu, Tian-Li; Goldberg, David E.; Sastry, Kumara; Lima, Cláudio F.; Pelikán, Martin

doi:10.1162/evco.2009.17.4.17409

Cited by 87 publications

(44 citation statements)

References 46 publications

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“…This is the case for several model-based efficiency enhancement techniques Pelikan and Sastry 2004;Sastry et al 2006;Lima et al 2005Lima et al , 2006Lima et al , 2009Lima 2009;Sastry et al 2005;Yu and Goldberg 2004;Yu et al 2007a; developed for EDAs that yield super-multiplicative speedups (Goldberg and Sastry 2010). Another important situation is the offline interpretation of the probabilistic models (Yu et al 2009;Yu and Goldberg 2004;Santana et al 2008) to help develop fixed but structure-based operators for specific instances or classes of problems that have similar structure. In this case the EDA can act as a data miner to gain insight about the problem.…”

Section: Introductionmentioning

confidence: 99%

Model accuracy in the Bayesian optimization algorithm

et al. 2010

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Evolutionary algorithms (EAs) are particularly suited to solve problems for which there is not much information available. From this standpoint, estimation of distribution algorithms (EDAs), which guide the search by using probabilistic models of the population, have brought a new view to evolutionary computation. While solving a given problem with an EDA, the user has access to a set of models that reveal probabilistic dependencies between variables, an important source of information about the problem. However, as the complexity of the used models increases, the chance of overfitting and consequently reducing model interpretability, increases as well. This paper investigates the relationship between the probabilistic models learned by the Bayesian optimization algorithm (BOA) and the underlying problem structure. The purpose of the paper is threefold. First, model building in BOA is analyzed to understand how the problem structure is learned. Second, it is shown how the selection operator can lead to model overfitting in Bayesian EDAs. Third, the scoring metric that guides the search for an adequate model structure is modified to take into account the non-uniform distribution of the mating pool generated by tournament selection. Overall, this paper makes a contribution towards understanding and improving model accuracy in BOA, providing more interpretable models to assist efficiency enhancement techniques and human researchers.

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Section: Introductionmentioning

confidence: 99%

Model accuracy in the Bayesian optimization algorithm

et al. 2010

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“…Linkage learning algorithms were classified by Yu et al [31] into three major categories: perturbation, interaction adaptation, and model building. Here, we include a fourth category, random methods, for a more complete treatment of various decomposition strategies in both conventional and coevolutionary algorithms.…”

Section: Classification Of Decomposition Strategiesmentioning

confidence: 99%

Cooperative Co-Evolution With Differential Grouping for Large Scale Optimization

Omidvar

Mei

et al. 2014

IEEE Trans. Evol. Computat.

625

367

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Abstract-Cooperative co-evolution has been introduced into evolutionary algorithms with the aim of solving increasingly complex optimization problems through a divide-and-conquer paradigm. In theory, the idea of co-adapted subcomponents is desirable for solving large-scale optimization problems. However, in practice, without prior knowledge about the problem, it is not clear how the problem should be decomposed. In this paper, we propose an automatic decomposition strategy called differential grouping that can uncover the underlying interaction structure of the decision variables and form subcomponents such that the interdependence between them is kept to a minimum. We show mathematically how such a decomposition strategy can be derived from a definition of partial separability. The empirical studies show that such near-optimal decomposition can greatly improve the solution quality on large-scale global optimization problems. Finally, we show how such an automated decomposition allows for a better approximation of the contribution of various subcomponents, leading to a more efficient assignment of the computational budget to various subcomponents.

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“…DSM clustering is a complicated task which requires expertise [3]. Different methods were introduced in the literature to extract clusters from a DSM [3,6] that suffer from different problems like oversimplified objective function (predicting good clustering) and restricting parameters like maximum number of clusters.…”

Section: Dsm Backgroundmentioning

confidence: 99%

A new DSM clustering algorithm for linkage groups identification

Nikanjam

Sharifi

Helmi

et al. 2010

Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation

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Linkage learning has been considered as an influential factor in success of genetic and evolutionary algorithms for solving difficult optimization problems. In this paper, a deterministic model named Dependency Structure Matrix (DSM) is used for explicitly decomposing the problem. DSM captures pair-wise dependencies of the problem that must be turned into higher order interactions while solving complex problems. One way to obtain these higher order interactions (linkage groups) is clustering the DSM. A new DSM clustering algorithm is proposed in this paper which is able to identify all the linkage groups from a less accurate DSM leading to a reduction in the number of fitness calls required for identifying the linkage groups. The proposed technique is tested on several benchmark problems and it is shown that it can accurately identify all the linkage groups by O(n 1.7 ) fitness evaluations, where n is problem size.

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Dependency Structure Matrix, Genetic Algorithms, and Effective Recombination

Cited by 87 publications

References 46 publications

Model accuracy in the Bayesian optimization algorithm

Model accuracy in the Bayesian optimization algorithm

Cooperative Co-Evolution With Differential Grouping for Large Scale Optimization

A new DSM clustering algorithm for linkage groups identification

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