Clique-detection models in computational biochemistry and genomics

Butenko, Sergiy; Wilhelm, Wilbert E.

doi:10.1016/j.ejor.2005.05.026

Cited by 151 publications

(89 citation statements)

References 42 publications

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“…Recent interest includes computational biochemistry, bio-informatics and genomics, see for example [11,8]. The problem is NP-hard and strong negative results have been shown about its approximability [9], making it an ideal testbed for search heuristics.…”

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confidence: 99%

Reactive and dynamic local search for max-clique: Engineering effective building blocks

Battiti

Mascia

2010

Computers & Operations Research

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This paper presents results of an ongoing investigation about how different algorithmic building blocks contribute to solving the Maximum Clique problem. We consider greedy constructions, plateau searches, and more complex schemes based on dynamic penalties and/or prohibitions, in particular the recently proposed technique of Dynamic Local Search and the previously proposed Reactive Local Search (RLS). We design a variation of the original RLS algorithm where the role of long-term memory is increased (RLS-LTM). In addition, we consider in detail the effect of the low-level implementation choices on the CPU time per iteration. We present experimental results on randomly generated graphs with different statistical properties, showing the crucial effects of the implementation, the robustness of different techniques, and their empirical scalability.

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confidence: 99%

Reactive and dynamic local search for max-clique: Engineering effective building blocks

Battiti

Mascia

2010

Computers & Operations Research

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“…In all levels the number of vertices depends on the connectivity in the instance graph, and in the instance depicted in Figure 3.1 are bounded from above by 20 k where k is the size of the cliques in the level, and 20 the number of nodes in the specific instance.…”

Section: The Techniquementioning

confidence: 99%

“…Recent interest includes computational biochemistry, bio-informatics and genomics, see for example [39,20,50].…”

Section: Applicationsmentioning

confidence: 99%

Analysis of reactive search optimisation techniques for the maximum clique problem and applications

Mascia¹

2011

4OR-Q J Oper Res

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This thesis introduces analysis tools for improving the current state of the art of heuristics for the Maximum Clique (MC) problem. The analysis focusses on algorithmic building blocks, on their contribution in solving hard instances of the MC problem, and on the development of new tools for the visualisation of search landscapes. As a result of the analysis on the algorithmic building blocks, we re-engineer an existing Reactive Local Search heuristic for the Maximum Clique (RLS-MC). We propose implementation and algorithmic improvements over the original RLS-MC aimed at faster restarts and greater diversification. The newly designed algorithm (RLS-LTM) is one order of magnitude faster than the original RLS-MC on some benchmark instances; but the proposed algorithmic changes impact also on the dynamically adjusted tabu tenure, which grows wildly on some hard instances. A more in depth analysis of the search dynamics of RLS-MC and RLS-LTM reveals the reasons behind the tabu tenure explosion and sheds some new light on the reactive mechanism. We design and implement RLS-fast which cures the issues with the tabu tenure explosion in RLS-LTM while retaining the performance improvement over RLS-MC.Moreover, building on the knowledge gained from the analysis, we propose a new hyper-heuristic which defines the new state of the art, and a novel supervised clustering technique based on a clique-finding component. Special thanks go to Thomas Stützle, Holger H. Hoos, Andrea Passerini and Wayne Pullan for giving me the opportunity to appreciate them both as teachers and collaborators.

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“…Here, finding a maximum clique in a graph has many important practical applications. These applications include coding theory [24], pattern recognition and image processing [9], [20], bioinformatics [5], [18], [33], design of wireless networks [16], and others [4], [36], [37].…”

Section: Introductionmentioning

confidence: 99%

A Much Faster Algorithm for Finding a Maximum Clique with Computational Experiments

Tomita

Matsuzaki

Nagao

et al. 2017

Journal of Information Processing

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Abstract:We present further improvements to a branch-and-bound maximum-clique-finding algorithm MCS (WALCOM 2010, LNCS 5942, pp.191-203) that was shown to be fast. First, we employ a variant of an efficient approximation algorithm KLS for finding a maximum clique. Second, we make use of appropriate sorting of vertices only near the root of the search tree. Third, we employ a lightened approximate coloring mainly near the leaves of the search tree. A new algorithm obtained from MCS with the above improvements is named k5 MCT. It is shown that k5 MCT is much faster than MCS by extensive computational experiments. In particular, k5 MCT is shown to be faster than MCS for gen400 p0.9 75, gen400 p0.9 65 and gen400 p0.9 55 by over 81,000, 39,000 and 19,000 times, respectively.

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Clique-detection models in computational biochemistry and genomics

Cited by 151 publications

References 42 publications

Reactive and dynamic local search for max-clique: Engineering effective building blocks

Reactive and dynamic local search for max-clique: Engineering effective building blocks

Analysis of reactive search optimisation techniques for the maximum clique problem and applications

A Much Faster Algorithm for Finding a Maximum Clique with Computational Experiments

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