An Investigative Study of Genetic Algorithms to Solve the DNA Assembly Optimization Problem

Bennaceur, Hachémi; Almutairy, Meznah; Alqhtani, Nora

doi:10.14569/ijacsa.2020.0111019

Cited by 2 publications

(5 citation statements)

References 28 publications

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“…This paper is a continuation of our previous work [4] to solve the DNA fragment assembly problem. As was pointed out in the introduction to this paper, the DNAFA is an optimization problem that attempts to reconstruct an original DNA sequence by finding the shortest DNA sequence from a given set of fragments.…”

Section: Discussionmentioning

confidence: 89%

“…The previous paper [4] showed that the DNAFA optimization problem is a special case of two well-known optimization problems: the traveling salesman problem and the quadratic assignment problem. Particularly, that paper theoretically demonstrated that all three optimization problems have a similar topological structure and that they need to explore a search space of solutions with the same complexity to find an optimal solution.…”

Section: Other Metaheuristics Algorithms For Dna Fragmentsmentioning

confidence: 99%

“…This section describe the different GA operations involved in the platform that suggested earlier [4]. This paper will study all these operations, test them experimentally, and try different versions by combining different tools from the platform to create the best version that will be compared with other algorithms from the literature.…”

Section: A the Ga Operations Of The Designed Platformmentioning

confidence: 99%

“…Several metaheuristic techniques have been developed to solve this problem [1], [2], [3]. This paper exploit the genetic algorithm platform (GAP) developed in the former preliminary paper [4], in which the existence of a polynomialtime reduction of DNAFA into the Traveling Salesman Problem and the Quadratic Assignment Problem was discussed. Then, conceptually designed a GA platform for solving the DNAFA problem, inspired by the existing efficient GAs in the literature for solving the TSP and QAP problems.…”

Section: Introductionmentioning

confidence: 99%

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Experimental Evaluation of Genetic Algorithms to Solve the DNA Assembly Optimization Problem

Bennaceur¹,

Almutairy²,

Alqhtani³

2023

IJACSA

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This paper aims to highlight the motivations for investigating genetic algorithms (GAs) to solve the DNA Fragment Assembly (DNAFA) problem. DNAFA problem is an optimization problem that attempts to reconstruct an original DNA sequence by finding the shortest DNA sequence from a given set of fragments. This paper is a continuation of our previous research paper in which the existence of a polynomialtime reduction of DNAFA into the Traveling Salesman Problem (TSP) and the Quadratic Assignment Problem (QAP) was discussed. Taking advantage of this reduction, this work conceptually designed a genetic algorithm (GA) platform to solve the DNAFA problem. This platform offers several ingredients enabling us to create several variants of GA solvers for the DNAFA optimization problems. The main contribution of this paper is the designing of an efficient GA variant by carefully integrating different GAs operators of the platform. For that, this work individually studied the effects of different GAs operators on the performance of solving the DNAFA problem. This study has the advantage of benefiting from prior knowledge of the performance of these operators in the contexts of the TSP and QAP problems. The best designed GA variant shows a significant improvement in accuracy (overlap score) reaching more than 172% of what is reported in the literature.

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Section: Discussionmentioning

confidence: 89%

Section: Other Metaheuristics Algorithms For Dna Fragmentsmentioning

confidence: 99%

Section: A the Ga Operations Of The Designed Platformmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Experimental Evaluation of Genetic Algorithms to Solve the DNA Assembly Optimization Problem

Bennaceur¹,

Almutairy²,

Alqhtani³

2023

IJACSA

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“…These types of methods combine and take advantage of metaheuristic techniques developed in artificial intelligence and existing statistical concepts designed for unsupervised feature selection that we are seeking to design in this paper. Genetic Algorithm (GA) is a powerful population-based search metaheuristic that has demonstrated its effectiveness and superiority in solving the famous hard combinatorial optimization problems the Quadratic Assignment Problem (QAP) [21,22], the Traveling Salesman Problem (TSP) [23,24], and the DNA Fragment Assembly Problem [25]. On the other hand, few GAs have been developed for the UFS problem and even those developed are very basic, in the sense that they used classical and generic known GA operators of the literature.…”

Section: Introductionmentioning

confidence: 99%

Genetic Algorithm Combined with the K-Means Algorithm: A Hybrid Technique for Unsupervised Feature Selection

Bennaceur,

Almutairy,

Alhussain

2023

Intelligent Automation &Amp; Soft Computing

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The dimensionality of data is increasing very rapidly, which creates challenges for most of the current mining and learning algorithms, such as large memory requirements and high computational costs. The literature includes much research on feature selection for supervised learning. However, feature selection for unsupervised learning has only recently been studied. Finding the subset of features in unsupervised learning that enhances the performance is challenging since the clusters are indeterminate. This work proposes a hybrid technique for unsupervised feature selection called GAk-MEANS, which combines the genetic algorithm (GA) approach with the classical k-Means algorithm. In the proposed algorithm, a new fitness function is designed in addition to new smart crossover and mutation operators. The effectiveness of this algorithm is demonstrated on various datasets. Furthermore, the performance of GAk-MEANS has been compared with other genetic algorithms, such as the genetic algorithm using the Sammon Error Function and the genetic algorithm using the Sum of Squared Error Function. Additionally, the performance of GAk-MEANS is compared with the stateof-the-art statistical unsupervised feature selection techniques. Experimental results show that GAk-MEANS consistently selects subsets of features that result in better classification accuracy compared to others. In particular, GAk-MEANS is able to significantly reduce the size of the subset of selected features by an average of 86.35% (72%-96.14%), which leads to an increase of the accuracy by an average of 3.78% (1.05%-6.32%) compared to using all features. When compared with the genetic algorithm using the Sammon Error Function, GAk-MEANS is able to reduce the size of the subset of selected features by 41.29% on average, improve the accuracy by 5.37%, and reduce the time by 70.71%. When compared with the genetic algorithm using the Sum of Squared Error Function, GAk-MEANS on average is able to reduce the size of the subset of selected features by 15.91%, and improve the accuracy by 9.81%, but the time is increased by a factor of 3. When compared with the machinelearning based methods, we observed that GAk-MEANS is able to increase the accuracy by 13.67% on average with an 88.76% average increase in time.

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An Investigative Study of Genetic Algorithms to Solve the DNA Assembly Optimization Problem

Cited by 2 publications

References 28 publications

Experimental Evaluation of Genetic Algorithms to Solve the DNA Assembly Optimization Problem

Experimental Evaluation of Genetic Algorithms to Solve the DNA Assembly Optimization Problem

Genetic Algorithm Combined with the K-Means Algorithm: A Hybrid Technique for Unsupervised Feature Selection

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