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Immunodominance clone selection algorithm (ICSA) is a robust and effective metaheuristic method for feature selection problem. However, ICSA is usually slow in finding the optimal solution. In this paper, we propose a parallel immunodominance clone selection algorithm (PICSA) on Graphics Processing Unit (GPU) to improve the speedup of ICSA for feature selection problem. The parallel program can considerably accelerate the feature selection operator. The immunodominance operator, which efficiently connects the local and global information, makes the algorithm able to jump out of the local optimum easily and obtain the global optimum. When comparing with other parallel languages, Open Computing Language (OpenCL) has advantages both in efficiency and portability. Therefore, we use OpenCL to implement this algorithm on Intel many integrated core and different GPU platforms. Experiment results obtained using highdimensional UCI machine learning and image texture datasets demonstrate that the PICSA algorithm allows one to achieve good acceleration ratio while maintaining similar classification accuracy to serial ICSA program. Besides, the OpenCL-based implementation of PICSA shows good portability on many integrated core and different GPU platforms as well.where P m is the mutation probability. Avg is the average affinity of all the antibodies in this iteration. P j is the actual mutation probability of Y i j . The value of P j will change around P m , when the affinity of AN OPENCL-ACCELERATED PARALLEL ICS ALGORITHM FOR FEATURE SELECTION 7 of 16Parallel heuristics algorithm could be divided into three major categories, which are master-slave, distributed, and cellular models [27]. Master-slave model fits in with parallel evolution algorithm on 8 of 16 H. ZHU ET AL.

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