Likage identification by perturbation and decision tree induction

Chuang, Chung-Yao; Chen, Ying-ping

doi:10.1109/cec.2007.4424493

Cited by 8 publications

(4 citation statements)

References 11 publications

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“…The behavior of these algorithms for other decomposable problems has been also studied from the perspective of ADFs [46,148]. Linkage identification methods have been analyzed using ADFs [19,24,128,165,190].…”

Section: Class Of Problemsmentioning

confidence: 99%

Gray-box optimization and factorized distribution algorithms: where two worlds collide

Santana

2017

Preprint

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The concept of gray-box optimization, in juxtaposition to black-box optimization, revolves about the idea of exploiting the problem structure to implement more efficient evolutionary algorithms (EAs). Work on factorized distribution algorithms (FDAs), whose factorizations are directly derived from the problem structure, has also contributed to show how exploiting the problem structure produces important gains in the efficiency of EAs. In this paper we analyze the general question of using problem structure in EAs focusing on confronting work done in gray-box optimization with related research accomplished in FDAs. This contrasted analysis helps us to identify, in current studies on the use problem structure in EAs, two distinct analytical characterizations of how these algorithms work. Moreover, we claim that these two characterizations collide and compete at the time of providing a coherent framework to investigate this type of algorithms. To illustrate this claim, we present a contrasted analysis of formalisms, questions, and results produced in FDAs and gray-box optimization. Common underlying principles in the two approaches, which are usually overlooked, are identified and discussed. Besides, an extensive review of previous research related to different uses of the problem structure in EAs is presented. The paper also elaborates on some of the questions that arise when extending the use of problem structure in EAs, such as the question of evolvability, high cardinality of the variables and large definition sets, constrained and multi-objective problems, etc. Finally, emergent approaches that exploit neural models to capture the problem structure are covered.

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Section: Class Of Problemsmentioning

confidence: 99%

Gray-box optimization and factorized distribution algorithms: where two worlds collide

Santana

2017

Preprint

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“…To provide the capability of handling BBs, BB identification and exchange have to be utilized. In this study, we focus on the mechanism of BB identification, generalize the concept of BB detection, and propose the use of inductive linkage identification [1] to detect general problem structures.…”

Section: Introductionmentioning

confidence: 99%

“…Then, a linkage identification technique, called inductive linkage identification [1], utilizing the ID3 decision tree [8] is adopted to detect global problem structures. The results indicate that the proposed technique can successfully detect problem structures, and the population requirement is insensitive to the problem structure consisting of similar sub-structures.…”

Section: Introductionmentioning

confidence: 99%

On the detection of general problem structures by using inductive linkage identification

Yuan-wei

Chen

2009

Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation

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Genetic algorithms and the descendant methods have been deemed robust and practical. To enhance the capabilities of genetic algorithms, tremendous effort has been invested in the field of evolutionary computation. One of the major trends to enhance genetic algorithms is to extract and exploit the relationship among variables, such as estimation of distribution algorithms and perturbation-based methods. In this study, we make an attempt to enable inductive linkage identification (ILI) to detect general problem structures, in which one variable may link to an arbitrary number of other variables. Our results indicate that the proposed technique can successfully detect the given problem structure.

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“…Traditionally this was thought of as building blocks, which are potentially disrupted by mutation and recombination operators [7]. There are several approaches to overcoming the problem of disruption of structure [3]:…”

Section: Introductionmentioning

confidence: 99%

Minimal walsh structure and ordinal linkage of monotonicity-invariant function classes on bit strings

Christie

McCall

Lonie

2014

Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation

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CopyrightItems in 'OpenAIR@RGU', Robert Gordon University Open Access Institutional Repository, are protected by copyright and intellectual property law. If you believe that any material held in 'OpenAIR@RGU' infringes copyright, please contact openair-help@rgu.ac.uk with details. The item will be removed from the repository while the claim is investigated. ABSTRACTProblem structure, or linkage, refers to the interaction between variables in a black-box fitness function. Discovering structure is a feature of a range of algorithms, including estimation of distribution algorithms (EDAs) and perturbation methods (PMs). The complexity of structure has traditionally been used as a broad measure of problem difficulty, as the computational complexity relates directly to the complexity of structure. The EDA literature describes necessary and unnecessary interactions in terms of the relationship between problem structure and the structure of probabilistic graphical models discovered by the EDA. In this paper we introduce a classification of problems based on monotonicity invariance. We observe that the minimal problem structures for these classes often reveal that significant proportions of detected structures are unnecessary. We perform a complete classification of all functions on 3 bits. We consider nonmonotonicity linkage discovery using perturbation methods and derive a concept of directed ordinal linkage associated to optimization schedules. The resulting refined classification factored out by relabeling, shows a hierarchy of nine directed ordinal linkage classes for all 3-bit functions. We show that this classification allows precise analysis of computational complexity and parallelizability and conclude with a number of suggestions for future work.

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Likage identification by perturbation and decision tree induction

Cited by 8 publications

References 11 publications

Gray-box optimization and factorized distribution algorithms: where two worlds collide

Gray-box optimization and factorized distribution algorithms: where two worlds collide

On the detection of general problem structures by using inductive linkage identification

Minimal walsh structure and ordinal linkage of monotonicity-invariant function classes on bit strings

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