Genetic Algorithm for Double Digest Problem

Sur-Kolay, Susmita; Banerjee, Sagnik; Mukhopadhyaya, Srabani; Murthy, C. A.

doi:10.1007/11590316_99

Cited by 2 publications

(23 citation statements)

References 9 publications

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“…To test the utility of DDmap, eight DDP instances, referred to as INS j ( j = 1⋯8), are taken from [13]. They are shown in the following Table 2:…”

Section: Resultsmentioning

confidence: 99%

“…Figure 5(a) and (b) are the comparison of the average running time between DDmap and the algorithm in 2005 [13] and 2012 [3]. Operator 1–5 are the crossover and mutation operator in DDmap.…”

Section: Discussionmentioning

confidence: 99%

“…Operator 1–5 are the crossover and mutation operator in DDmap. Because the crossover operator in [13] is the same as our operator 2 and the two mutation operators in [3] are similar to our operators op4 and op5, so we only implement the mutation operator op6 in [13] and crossover operator op7 in [3]. Eight instances are from the paper [13].…”

Section: Discussionmentioning

confidence: 99%

“…The main motivation of this work comes from three considerations: First, almost all existing formulations of the DDP problem use multiset as the basic data structure, while we find that it is even easier to model the DDP problem by using vectors. Second, some recently proposed genetic algorithms [3, 13] for addressing the DDP problem should be improved. Third, it is of interest to develop an open-source package for studying the DDP problem by using easily accessible engineering computation platforms, such as MATLAB.…”

Section: Introductionmentioning

confidence: 99%

“…Our main contributions are summarized as follows:A vector-based formulation of the DDP problem is presented and illustrated step-by-step.A novel genetic algorithm for solving the DDP problem is proposed by combining six genetic operators, and a MATLAB package, DDmap, is implemented by integrating the proposed genetic algorithm and other necessary supporting and testing widgets. Then, by using DDmap, exact solutions for typical DDP test instances [13] are explicitly derived and depicted. (See the right column of Table 1.…”

Section: Introductionmentioning

confidence: 99%

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DDmap: a MATLAB package for the double digest problem using multiple genetic operators

et al. 2019

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Background In computational biology, the physical mapping of DNA is a key problem. We know that the double digest problem (DDP) is NP-complete. Many algorithms have been proposed for solving the DDP, although it is still far from being resolved. Results We present DDmap, an open-source MATLAB package for solving the DDP, based on a newly designed genetic algorithm that combines six genetic operators in searching for optimal solutions. We test the performance of DDmap by using a typical DDP dataset, and we depict exact solutions to these DDP instances in an explicit manner. In addition, we propose an approximate method for solving some hard DDP scenarios via a scaling-rounding-adjusting process. Conclusions For typical DDP test instances, DDmap finds exact solutions within approximately 1 s. Based on our simulations on 1000 random DDP instances by using DDmap, we find that the maximum length of the combining fragments has observable effects towards genetic algorithms for solving the DDP problem. In addition, a Maple source code for illustrating DDP solutions as nested pie charts is also included.

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“…To test the utility of DDmap, eight DDP instances, referred to as INS j ( j = 1⋯8), are taken from [13]. They are shown in the following Table 2:…”

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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DDmap: a MATLAB package for the double digest problem using multiple genetic operators

et al. 2019

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Quantum Inspired Genetic Algorithm for Double Digest Problem

Suo

Pan

et al. 2020

IEEE Access

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The double digest problem (DDP) is a fundamental problem in bioinformatics, and it has been proven to be an NP-hard problem. As a type of promising combinational optimization method inspired by evolutionary theory, genetic algorithms have attracted much attention during the past four decades, and some genetic algorithms have been successfully applied to solve the DDP. To further enhance the ability to solve the DDP, it is of interest to couple the insights from classical genetic algorithms with the parallel capability of quantum computation. Thus, in this paper, we propose a quantum inspired genetic algorithm (QIGA) for the DDP. In our QIGA, the binary Q-bit representation is converted to mapping sequences, and on this basis, DNA fragments are reordered. The solution of the DDP is a permutation, selected by the QIGA, of all the DNA fragments. The effectiveness and efficiency of our proposal can be inferred from the simulation results for the QIGA on a classical computer. As far as we know, this is the first attempt to address the DDP using a QIGA. Compared with classical genetic algorithms for the DDP, our QIGA slightly accelerates the time require to solve the problem, and we believe that it will achieve superior performance when a quantum computer with the required scale is available. INDEX TERMS Double digest problem, quantum inspired genetic algorithm, DNA mapping.

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Genetic Algorithm for Double Digest Problem

Cited by 2 publications

References 9 publications

DDmap: a MATLAB package for the double digest problem using multiple genetic operators

DDmap: a MATLAB package for the double digest problem using multiple genetic operators

Quantum Inspired Genetic Algorithm for Double Digest Problem

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