Abstract
Genetic Algorithm (GA) is an evolutionary meta-heuristic approach, motivated by the principle of Genetics and natural selection. The goal of GA is to produce better offspring by genetic operations including selection, crossover, and mutation. In GA, parent selection is essential as the optimization results directly depend on the fitness of next generation (off-springs). In this paper, an improved version of GA, named Elitist Twin Removal Genetic Algorithm (ETRGA) has been proposed to enhance the performance of crossover operator during parent selection. This is to ensure that the best gene template will never be lost. In addition, Twin Removal (TR) operator efficiently balances the intensification (exploitation) and diversification (exploration) of the search process. Proposed ETRGA has been applied to 15 well-known benchmark functions as well as gene selection problem to find critical gene. Here, common disease gene obtained by three algorithms is termed as a critical gene. The performance of ETRGA has been compared with Simple Genetic Algorithm (SGA) and Twin Removal Genetic Algorithm (TRGA). The experimental results confirm that the proposed ETRGA outperforms SGA and TRGA in terms of statistical metrics taken care in the account in benchmark functions and real-life problem. The convergence graph of ETRGA shows that it has better exploration and does not suffer from premature convergence.
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Das, P., Jana, B., Acharyya, S. (2021). A New Variant of Genetic Algorithm for Solving Gene Selection Problem. In: Giri, D., Buyya, R., Ponnusamy, S., De, D., Adamatzky, A., Abawajy, J.H. (eds) Proceedings of the Sixth International Conference on Mathematics and Computing. Advances in Intelligent Systems and Computing, vol 1262. Springer, Singapore. https://doi.org/10.1007/978-981-15-8061-1_25
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DOI: https://doi.org/10.1007/978-981-15-8061-1_25
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