Decomposition Algorithms for Training Large-Scale Semiparametric Support Vector Machines

Lee, Sangkyun; Wright, Stephen J.

doi:10.1007/978-3-642-04174-7_1

Cited by 2 publications

(1 citation statement)

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“…In constructing a mutation operation, we considered computational complexity and the idea that retaining many aspects of a current solution could lead to better new solutions, particularly for problems which are fully or partly separable. This approach is related to decomposition into subproblems utilised by Liu and Rubin [9], and to Gibbs sampling [18].…”

Section: Decomposition Mutation Operationmentioning

confidence: 99%

TAM-EDA: Multivariate t Distribution, Archive and Mutation Based Estimation of Distribution Algorithm

Gao¹,

Wood

2014

ANZIAMJ

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We present a novel estimation of a distribution algorithm (eda), tam-eda, which uses a multivariate t distribution model, an archive population and a mutation operation to escape local minima, avoid premature convergence and utilize a record of the best solutions. Earlier edas used multivariate normal distributions to model low-cost regions of the search space. The multivariate t distribution has heavier tails and so is more likely to maintain diversity, while still allowing convergence to occur. The current population of potential solutions has limited ability to represent all the best regions of the search space explored so far. The archive allows storage of a larger population of promising solutions, which are then used in model building. However, the eda Contents C721model and archive may still become stuck at suboptimal solutions, so to combat this we introduce a decomposition mutation operation which retains most of the attributes of a current solution but attempts large changes in others. A comparison with generic eda, genetic algorithms and the Nelder-Mead method shows that tam-eda is an effective optimization algorithm for a range of test problems.

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Section: Decomposition Mutation Operationmentioning

confidence: 99%