Higher Epistasis in Genetic Algorithms

Iglesias, Marcos; Peñaranda, V. S.; Vidal, Concepción; Verschoren, A.

doi:10.1017/s0004972708000233

Cited by 3 publications

(2 citation statements)

References 4 publications

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“…The generalisation of this concept to A;-epistasis (k € N) can be found in [8]. The general case is much more technical, so we preferred to stick to the more intuitive case k = 2, in the present note.…”

Section: Fia»i>»k)-(t-2) E /Fw-mentioning

confidence: 99%

The 2-epistasis of fitness functions

Iglesias

Peñaranda

Vidal

et al. 2007

Bull. Austral. Math. Soc.

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In this note about Genetic Algorithms(GA-), we study the2-epistasisof a fitness function over a search space. This concept is a natural generalisation of that ofepistasis, previously considered by Davidor in 1991 Suys and Verschoren in 1996 and Van Hove and Verschoren in 1994 for example. We completely characterise fitness functions whose 2-epistasis is minimal: these are exactly the second order functions. The validity of 2-epistasis as a measure of hardness with respect to genetic algorithms is checked over some classical laboratory functions. Finally, we obtain an upper bound of the maximal value of the 2-epistasis when we restrict attention to non-negative functions.

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Section: Fia»i>»k)-(t-2) E /Fw-mentioning

confidence: 99%

The 2-epistasis of fitness functions

Iglesias

Peñaranda

Vidal

et al. 2007

Bull. Austral. Math. Soc.

Self Cite

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“…The resulting models include genetic programming [8], Random Forests [9], neural networks [10], Bayesian Networks [11], [12], [13], greedy search [14], information theoretical approaches [15] and Multifactor Dimensionality Reduction (MDR) [16].…”

Section: Introductionmentioning

confidence: 99%

Detecting epistasis in the presence of linkage disequilibrium: A focused comparison

Ibrahim

Newhouse

Dobson

2013

2013 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)

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Abstract-We present results from a comparison of three epistasis-detection tools using large-scale simulated genetic data: SNPHarvester, SNPRuler and Ambience. The tools were chosen based on their merits to be representative of the state of the art of epistasis detection. We design and conduct experiments to test the performance of the methods in detecting interacting loci or their proxies in linkage disequilibrium (LD) tagged regions, in datasets containing simulated 2,3 and 4-way epistatic interactions.The results show that SNPHarvester is the fastest while Ambience is the most robust. Moreover, SNPRuler provides the best power, specially with higher-level interactions, but cannot scale-up to larger datasets.

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Helper-objective optimization strategies for the Job-Shop Scheduling Problem

Lochtefeld

Ciarallo

2011

Applied Soft Computing

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Higher Epistasis in Genetic Algorithms

Cited by 3 publications

References 4 publications

The 2-epistasis of fitness functions

The 2-epistasis of fitness functions

Detecting epistasis in the presence of linkage disequilibrium: A focused comparison

Helper-objective optimization strategies for the Job-Shop Scheduling Problem

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