Genetic algorithm solution for double digest problem

Ganjtabesh, Mohammad; Ahrabian, H.; Nowzari-Dalini, Abbas

doi:10.6026/97320630008453

Cited by 5 publications

(26 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To verify the effectiveness and performance of the QIGA for the double digest problem, eight typical instances and random instances are tested for the QIGA. The QIGA and GAs in [8], [9] and [16] are compared by testing eight typical instances. In these experiments, the population size is N = 50, the maximum evolution generation is 10000, the crossover probability is pc = 0.85, and the mutation probability is 0.45 ∼ 0.55.…”

Section: Resultsmentioning

confidence: 99%

“…In summary, the QIGA is very effective for solving the DDP. [8], GM12 is the crossover operator in [9], and crossover operator DDmapc and mutation operator DDmapm are from [18].…”

Section: B Random Instancesmentioning

confidence: 99%

“…In 2005, Sur-Kolay et al [8] presented a genetic algorithm for solving the DDP. Ganjtabesh et al [9] improved Sur-Kolay's algorithm, which could only handle errorless data, and extended it to erroneous data. In 2019, Wang et al [17] developed a MATLAB package for the DDP using multiple genetic operators.…”

Section: Introductionmentioning

confidence: 99%

“…We propose a quantum inspired genetic algorithm (QIGA) for the double digest problem. The QIGA uses binary Q-bits to encode chromosomes; then, eight typical instances [8] and some random instances are tested, and comparison experiments are carried out between the QIGA and the algorithms from [8], [9], [16].…”

Section: Introductionmentioning

confidence: 99%

“…• Comparative experiments are carried out. By comparing the QIGA with the algorithm from [9], the QIGA is proven to be more effective. The remaining sections of this paper are organized as follows.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Quantum Inspired Genetic Algorithm for Double Digest Problem

Suo

Pan

et al. 2020

IEEE Access

View full text Add to dashboard Cite

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.

show abstract

Section: Resultsmentioning

confidence: 99%

“…In summary, the QIGA is very effective for solving the DDP. [8], GM12 is the crossover operator in [9], and crossover operator DDmapc and mutation operator DDmapm are from [18].…”

Section: B Random Instancesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Quantum Inspired Genetic Algorithm for Double Digest Problem

Suo

Pan

et al. 2020

IEEE Access

View full text Add to dashboard Cite

show abstract

An idea to explore: A systematic approach for solving plasmid double‐digest puzzles

Callahan,

Smith

2024

Biochem Molecular Bio Educ

View full text Add to dashboard Cite

A common exercise given to students early in a molecular biology course is the creation of a restriction map of a plasmid “digested” by two restriction enzymes (RE). Meanwhile, students have learned from an early age about the properties and analyses of circles in their mathematics courses. But it is rare for students to learn using puzzle‐based assignments at the intersection of molecular biology and mathematics. Therefore, we should present students with a puzzle that allows them to combine knowledge and skills from these seemingly disconnected disciplines. Here, we present a method for analyzing RE digests of circular plasmids using basic geometric principles.

show abstract

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

et al. 2019

View full text Add to dashboard Cite

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.

show abstract

Genetic algorithm solution for double digest problem

Cited by 5 publications

References 3 publications

Quantum Inspired Genetic Algorithm for Double Digest Problem

Quantum Inspired Genetic Algorithm for Double Digest Problem

An idea to explore: A systematic approach for solving plasmid double‐digest puzzles

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

Contact Info

Product

Resources

About