DNA Sequences Optimization Based on Gravitational Search Algorithm for Reliable DNA Computing

Xiao, Jianhua; Cheng, Zhen

doi:10.1109/bic-ta.2011.12

Cited by 16 publications

(5 citation statements)

References 19 publications

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“…A new multiobjective gravitational search algorithm was proposed in [4]. The GSA shows satisfactory results for solving many problems in a various applications; Solving Symmetric Traveling Salesman Problem [7], solving the flow shop scheduling problem [8], in feature selection [9], image enhancement [10], solving DNA sequence design problem [11], and optimize the filter modeling parameters [12]. A hybrid algorithm was derived from both Genetic Algorithms and Gravitational Search Algorithm for feature set selection [13].…”

Section: Literature Reviewmentioning

confidence: 99%

On The Performance of the Gravitational Search Algorithm

Eldos¹,

Qasim²

2013

IJACSA

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Abstract-Gravitational Search Algorithms (GSA) are heuristic optimization evolutionary algorithms based on Newton's law of universal gravitation and mass interactions. GSAs are among the most recently introduced techniques that are not yet heavily explored. An early work of the authors has successfully adapted this technique to the cell placement problem, and shown its efficiency in producing high quality solutions in reasonable time. We extend this work by fine tuning the algorithm parameters and transition functions towards better balance between exploration and exploitation. To assess its performance and robustness, we compare it with that of Genetic Algorithms (GA), using the standard cell placement problem as benchmark to evaluate the solution quality, and a set of artificial instances to evaluate the capability and possibility of finding an optimal solution. Experimental results show that the proposed approach is competitive in terms of success rate or likelihood of optimality and solution quality. And despite that it is computationally more expensive due to its hefty mathematical evaluations, it is more fruitful on the long run.

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Section: Literature Reviewmentioning

confidence: 99%

On The Performance of the Gravitational Search Algorithm

Eldos¹,

Qasim²

2013

IJACSA

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“…2 " is neglected in (17) as the coils have the same current density. The field produced is the superposition of the field produced by the main coil, and that does not contain the optimization parameters so the correcting coils fields can be defined:…”

Section: Pseudo Code Of Igsamentioning

confidence: 99%

“…GSA is a novel technique that depends on the second law of motion and newton law of gravity. This algorithm emanates under the populationbased algorithm having agents of different masses [3] and has been applied to the number of the applications including power engineering [4][5][6][7], image processing [8][9][10], communication [11][12][13], controls [14][15][16], biology [17,18], and computer science [19]. Every agent in GSA is simulated as a matter and problem search space as the universe where each agent subject to the gravitational force.…”

Section: Introductionmentioning

confidence: 99%

An improved gravitational search algorithm for solving an electromagnetic design problem

Khan

Ling

2020

J Comput Electron

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Gravitational Search Algorithm (GSA) is a novel optimization technique that depends upon the law of motion and law of gravity of masses interaction between the agents. GSA has shown outstanding performance but has a drawback of having a slow exploitation process due to the fitness function influence on masses of the agents. As a result, after each iteration, the masses are getting heavier and restricts the movement. For that reason, masses cancel out the gravitational forces on each other and restricting them from quickly exploiting the optimum. To overcome this limitation, an improved GSA (IGSA) with a modified exploitation strategy is proposed in this paper. The primary aim of modification is to enhance the proficiency of the algorithm in terms of faster convergence and avoid premature convergence. An electromagnetic optimization problem is used to validate the performance of the presented method. Simulation results confirmed that the proposed method has outstanding results in solving the Loney's Solenoid design problem and solution stability is much better as compared to the Standard gravitational search algorithm and various other state-of-the-art techniques previously applied for solving this problem.

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“…Continuity is usually used to depict continuous degrees of the same base in DNA sequences. The calculated formula is shown as follows [20]:…”

Section: B Specific Constraints To Evaluate Quality Of Dna Encodingmentioning

confidence: 99%

Design of DNA Coding Based on Immune Clone Selection Algorithm

Huang¹,

Liguo²,

Tang³

2012

JSW

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In DNA computing, the design of DNA code is the most important step. The definition of DNA code, the constraints of DNA encoding and the corresponding formula are firstly described in this article, and then the design of DNA encoding based on immune clone algorithm is introduced. Simulation experiments show that DNA molecular chains that satisfy specific constraints and the corresponding thermodynamic characteristics can be produced based on immune clone selection algorithm. Using immune clone selection algorithm can improves the efficiency of encoding by, and using a series of constraints can produce very stable DNA sequences. It laid a good foundation for DNA computing.

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DNA Sequences Optimization Based on Gravitational Search Algorithm for Reliable DNA Computing

Cited by 16 publications

References 19 publications

On The Performance of the Gravitational Search Algorithm

On The Performance of the Gravitational Search Algorithm

An improved gravitational search algorithm for solving an electromagnetic design problem

Design of DNA Coding Based on Immune Clone Selection Algorithm

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