Linkage Identification by Nonlinearity Check for Real-Coded Genetic Algorithms

Tezuka, M.; Munetomo, Masaharu; Akama, Kiyoshi

doi:10.1007/978-3-540-24855-2_20

Cited by 27 publications

(20 citation statements)

References 4 publications

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“…Below we will give some basic consideration about linkage detection in continuous domain. A research LINC-R based on Munetomo et al's previous work [12] has been proposed elsewhere [15], which tests nonlinearity by order-2 perturbations for real-coded GA.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Extended probe method for linkage discovery over high-cardinality alphabets

Zhou

Sun

Heckendorn

2007

Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation

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The work addresses the problem of identifying the epistatic linkage of a function from high cardinality alphabets to the real numbers. It is a generalization of Heckendorn and Wright's work that restricts problem representation into the binary-string domain. Discrete Fourier transform is used to analyze underlying structure in high-cardinality alphabets space. Boolean operators are replaced with new operators such as ⊕, , ⊗ and so on in high cardinality alphabets. The "probe" formulation is redesigned to determine epistatic properties of non-binary function. Theoretical analysis shows the close relationship between probe value and problem structure. A deterministic and a stochastic algorithm based on properties of probes are proposed to completely identify the linkage structure and rigourously compute all Fourier coefficients. Some discussion about linkage detection for continuous problems is given.

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Section: Conclusion and Discussionmentioning

confidence: 99%

Extended probe method for linkage discovery over high-cardinality alphabets

Zhou

Sun

Heckendorn

2007

Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation

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“…Several algorithms have been proposed for automatic identification of variable interaction [10], [12], [19]. Differential grouping [10] is the state-of-the-art decomposition algorithm that can identify non-separable and separable variables with high accuracy.…”

Section: Introductionmentioning

confidence: 99%

Effective decomposition of large-scale separable continuous functions for cooperative co-evolutionary algorithms

Omidvar

Mei

2014

2014 IEEE Congress on Evolutionary Computation (CEC)

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Abstract-In this paper we investigate the performance of cooperative co-evolutionary (CC) algorithms on large-scale fullyseparable continuous optimization problems. We have shown that decomposition can have significant impact on the performance of CC algorithms. The empirical results show that the subcomponent size should be chosen small enough so that the subcomponent size is within the capacity of the subcomponent optimizer. In practice, determining the optimal size is difficult. Therefore, adaptive techniques are desired by practitioners. Here we propose an adaptive method, MLSoft, that uses widely-used techniques in reinforcement learning such as the value function method and softmax selection rule to adapt the subcomponent size during the optimization process. The experimental results show that MLSoft is significantly better than an existing adaptive algorithm called MLCC on a set of large-scale fully-separable problems.

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“…Algorithms that rely on perturbation include mGA [14], fmGA [33], gemGA [34], LINC [35], and LIMD [36]. These methods are typically incorporated into a binary GA. A limited number of techniques have also been developed for real-valued GAs, such as LINC-R [37]. However, the experimental results for LINC-R were limited to low dimensional functions with up to 40 dimensions.…”

Section: Classification Of Decomposition Strategiesmentioning

confidence: 99%

“…Using the interpretation given in this section, we can show that the heuristic used in LINC-R [37] can be derived by applying Theorem 1.…”

Section: A Differential Grouping Algorithmmentioning

confidence: 99%

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Cooperative Co-Evolution With Differential Grouping for Large Scale Optimization

Omidvar

Mei

et al. 2014

IEEE Trans. Evol. Computat.

605

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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Linkage Identification by Nonlinearity Check for Real-Coded Genetic Algorithms

Cited by 27 publications

References 4 publications

Extended probe method for linkage discovery over high-cardinality alphabets

Extended probe method for linkage discovery over high-cardinality alphabets

Effective decomposition of large-scale separable continuous functions for cooperative co-evolutionary algorithms

Cooperative Co-Evolution With Differential Grouping for Large Scale Optimization

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