01929nas a2200265   4500000000100000000000100001008004100002260001300043100001800056700002000074700002500094700001700119700001600136700001800152700002500170700001600195700001900211700002000230700002400250245010600274856007200380490000800452520118900460022001401649       2021                            d  c11/230211 aMohammad Roui1 aMariam Zomorodi1 aMasoomeh Sarvelayati1 aMoloud Abdar1 aHamid Noori1 aPawel Plawiak1 aRyszard Tadeusiewicz1 aXujuan Zhou1 aAbbas Khosravi1 aSaeid Nahavandi1 aU. Rajendra Acharya00aA novel approach based on genetic algorithm to speed up the discovery of classification rules on GPUs  uhttps://www.sciencedirect.com/science/article/pii/S095070512100681X0 v2313 a<p>This paper proposes a new approach to produce classification rules based on evolutionary computation with novel crossover and mutation operators customized for execution on graphics processing unit (GPU). Also, a novel method is presented to define the fitness function, i.e. the function which measures quantitatively the accuracy of the rule. The proposed fitness function is benefited from parallelism due to the parallel execution of data instances. To this end, two novel concepts; coverage matrix and reduction vectors are used and an altered form of the reduction vector is compared with previous works. Our CUDA program performs operations on coverage matrix and reduction vector in parallel. Also these data structures are used for evaluation of fitness function and calculation of genetic operators in parallel. We proposed a vector called average coverage to handle crossover and mutation properly. Our proposed method obtained a maximum accuracy of 99.74% for Hepatitis C Virus (HCV) dataset, 95.73% for Poker dataset, and 100% for COVID-19 dataset. Our speedup is higher than 20% for HCV and COVID-19, and 50% for Poker, compared to using single core processors.</p>
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