Linkage Identification by Fitness Difference Clustering

Tsuji, Masashi; Munetomo, Masaharu; Akama, Kiyoshi

doi:10.1162/evco.2006.14.4.383

Cited by 17 publications

(11 citation statements)

References 4 publications

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“…Such results indicate that ILI is more efficient than LINC, O(' 2 ) ¼ O(k 2 m 2 ) (Munetomo and Goldberg 1998), and similar to D 5 , O(') ¼ O(km) (Tsuji et al 2006), where ' is the problem size, k is the size (i.e. length or order) of BBs, and m is the number of BBs.…”

Section: Inductive Linkage Identificationmentioning

confidence: 83%

“…An interesting approach combining the ideas of EDAs and perturbation methods, called the dependency detection for distribution derived from fitness differences (D 5 ), was developed by Tsuji, Munetomo, and Akama (2006). D 5 detects the dependencies of variables by estimating the distributions of strings clustered according to fitness differences.…”

Section: Introductionmentioning

confidence: 99%

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Inductive linkage identification on building blocks of different sizes and types

Chen

Chuang

Yuan-wei

2012

International Journal of Systems Science

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The goal of linkage identification is to obtain the dependencies among decision variables. Such information or knowledge can be applied to design crossover operators and/or the encoding schemes in genetic and evolutionary methods. Thus, promising sub-solutions to the problem will be disrupted less likely, and successful convergence may be achieved more likely. To obtain linkage information, a linkage identification technique, called Inductive Linkage Identification (ILI), was proposed recently. ILI was established upon the mechanism of perturbation and the idea of decision tree learning. By constructing a decision tree according to decision variables and fitness difference values, the interdependent variables will be determined by the adopted decision tree learning algorithm. In this article, we aim to acquire a better understanding on the characteristics of ILI, especially its behaviour under problems composed of different-sized and different-type building blocks (BBs) which are not overlapped. Experiments showed that ILI can efficiently handle BBs of different sizes and is insensitive to BB types. Our experimental observations indicate the flexibility and the applicability of ILI on various elementary BB types that are commonly adopted in related experiments.

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Section: Inductive Linkage Identificationmentioning

confidence: 83%

Section: Introductionmentioning

confidence: 99%

Inductive linkage identification on building blocks of different sizes and types

Chen

Chuang

Yuan-wei

2012

International Journal of Systems Science

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“…Estimation of Distribution Algorithms (EDAs) (Larrañaga and Lozano 2002) that employ this technique are (Emmendorfer and Pozo 2009;Pelikan and Goldberg 2000;Tsuji et al 2006;Bosman and Thierens 2002).…”

Section: Clustering In Single Objective Optimizationmentioning

confidence: 99%

An investigation of clustering strategies in many-objective optimization: the I-Multi algorithm as a case study

et al. 2017

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A variety of general strategies have been applied to enhance the performance of multi-objective optimization algorithms for many-objective optimization problems (those with more than three objectives). One of these strategies is to split the solutions to cover di↵erent regions (clusters) and apply an optimizer to each region with the aim of producing more diverse solutions and achieving a better distributed approximation of the Pareto front. However, the e↵ectiveness of clustering in this context depends on a number of issues, including the characteristics of the objective functions. In this paper we show how the choice of the clustering strategy can greatly influence the behavior an optimizer. We try to find a relation between the characteristics a of multi-objective optimization problem (MOP) and the e ciency of the use of a clustering type in its resolution. Using as a case study, the Iterated Multi-swarm (I-Multi), a recently introduced multi-objective particle swarm optimization (MOPSO) algorithm, we scrutinize the impact that clustering in di↵erent spaces (of variables, objectives, and a combination of both) can have on the approximations of the Pareto front. Furthermore, using two difficult function benchmarks of problems of up to 20 objectives, we evaluate the e↵ect of using di↵erent metrics for determining the similarity between the solutions during the clustering process. Our results confirm the important e↵ect of the clustering strategy on the behavior of multi-objective optimizers. Moreover, we present evidence that some problem characteristics can be used to select the most e↵ective clustering strategy, significantly improving the quality of the Pareto front approximations produced by I-Multi.

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“…the size of the maximum definition set of the function is bounded) [121]. It has been stated that for exponentiallyscaled problems [114] perturbation methods can be an alternative to probabilistic modeling to detect the interactions. They have been combined with probabilistic methods used by EDAs to capture and exploit a more accurate model of the interactions [114,121].…”

Section: Learning the Structurementioning

confidence: 99%

Research topics in discrete estimation of distribution algorithms based on factorizations

2008

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In this paper, we identify a number of topics relevant for the improvement and development of discrete estimation of distribution algorithms. Focusing on the role of probability distributions and factorizations in estimation of distribution algorithms, we present a survey of current challenges where further research must provide answers that extend the potential and applicability of these algorithms. In each case we state the research topic and elaborate on the reasons that make it relevant for estimation of distribution algorithms. In some cases current work or possible alternatives for the solution of the problem are discussed.

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Linkage Identification by Fitness Difference Clustering

Cited by 17 publications

References 4 publications

Inductive linkage identification on building blocks of different sizes and types

Inductive linkage identification on building blocks of different sizes and types

An investigation of clustering strategies in many-objective optimization: the I-Multi algorithm as a case study

Research topics in discrete estimation of distribution algorithms based on factorizations

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