Edit metric decoding: Representation strikes back

Hughes, James Alexander; Brown, Joseph Alexander; Houghten, Sheridan; Ashlock, Daniel

doi:10.1109/cec.2013.6557575

Cited by 7 publications

(16 citation statements)

References 18 publications

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“…Study [13] improved upon previous results obtained with evolutionary algorithms for DNA Error Correction. This study evolved Side Effect Machines which are an extension of Finite State Machines that associate a side effect with each state transition.…”

Section: Previous Worksupporting

confidence: 80%

“…Similar to the work done in [10][11][12][13] which had significant success with the Recentering-Restarting Genetic Algorithm, this work will use the variations of the Recentering-Restarting Genetic Algorithm. The reasoning for the use of both representations discussed includes the ability to avoid fixating on local optima and to provide a structured sequential search through the fitness landscape for a global optima.…”

Section: Recentering-restarting Genetic Algorithmmentioning

confidence: 94%

“…This method has also been studied in the past to evolve Side Effect Machines for the use of performing DNA Error Correction [13]. In all of these works the Recentering-Restarting Genetic Algorithm has been able to produce significant results.…”

Section: Previous Workmentioning

confidence: 97%

“…This study evolved Side Effect Machines which are an extension of Finite State Machines that associate a side effect with each state transition. The methodologies used in [13] are very similar to the approach taken in this study.…”

Section: Previous Workmentioning

confidence: 97%

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Recentering and restarting a genetic algorithm using a generative representation for an ordered gene problem1

Hughes

Houghten

Ashlock

2014

HIS

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Recentering-Restarting Genetic Algorithms have been used successfully to evolve multiple epidemic networks and perform DNA error correction. This work studies variations of the Recentering-Restarting Genetic Algorithm for the purpose of evaluating its effectiveness for ordered gene problems. These variations use multiple seeds and two adaptive representations which use generating sets to produce local search. These algorithm variations are applied to what many considered the quintessential ordered gene problem, the Travelling Salesman Problem. Two distinct sets of experimental analysis was performed: first, using large problem instances to determine the effectiveness of the Recentering-Restarting Genetic Algorithm in comparison to benchmarks and second, studying many small problem instances ranging from 12 to 20 cities to determine if any one of the algorithm variations always outperforms the others. These algorithm variations were comparable to highly competitive optimization algorithms submitted to the DIMACS TSP implementation challenge. In studying the small problem instances, it was observed that no one algorithm always dominates on all problem instances within a domain. This study demonstrates how the Recentering-Restarting Genetic Algorithm is a useful tool for improving upon results generated by other powerful heuristics.1 This paper is an extended version of the paper: "Recentering, Reanchoring and Restarting an Evolutionary Algorithm," in proceed-

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Section: Previous Worksupporting

confidence: 80%

Section: Recentering-restarting Genetic Algorithmmentioning

confidence: 94%

Section: Previous Workmentioning

confidence: 97%

Section: Previous Workmentioning

confidence: 97%

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Recentering and restarting a genetic algorithm using a generative representation for an ordered gene problem1

Hughes

Houghten

Ashlock

2014

HIS

Self Cite

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“…The RRGA has been studied before in multiple works by D. Ashlock et al studying epidemic networks [32], Side Effect Machines for decoding [33], and the travelling salesman problem [34]. In all of these works the RRGA was able to produce significant results.…”

Section: ) Recentering-restarting Genetic Algorithmmentioning

confidence: 94%

Recentering and Restarting Genetic Algorithm variations for DNA Fragment Assembly

Hughes¹,

Houghten²,

Mallén-Fullerton³

et al. 2014

2014 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology

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The Fragment Assembly Problem is a major component of the DNA sequencing process that is identified as being NP-Hard. A variety of approaches to this problem have been used, including overlap-layout-consensus, de Bruijn graphs, and greedy graph based algorithms. The overlap-layout-consensus approach is one of the more popular strategies which has been studied on a collection of heuristics and metaheuristics.In this study heuristics and Genetic Algorithm variations are combined to exploit their respective benefits. These algorithms were able to produce results that surpassed the best results obtained by a collection of state-of-the-art metaheuristics on ten of sixteen popular benchmark data sets.

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