2024-03-29T16:38:21Zhttps://ipsj.ixsq.nii.ac.jp/ej/?action=repository_oaipmhoai:ipsj.ixsq.nii.ac.jp:000828332024-03-29T05:26:34Z01164:02735:06701:06817
GAROP: Genetic Algorithm framework for Running On Parallel environmentsGAROP: Genetic Algorithm framework for Running On Parallel environmentsenghttp://id.nii.ac.jp/1001/00082831/Technical Reporthttps://ipsj.ixsq.nii.ac.jp/ej/?action=repository_action_common_download&item_id=82833&item_no=1&attribute_id=1&file_no=1Copyright (c) 2012 by the Information Processing Society of JapanFaculty of Life and Medical Sciences, Doshisha UniversityGraduate School of Engineering, Doshisha UniversityFaculty of Science and Engineering, Doshisha UniversityFaculty of Science and Engineering, Doshisha UniversityTomoyuki, HiroyasuRyosuke, YamanakaMasato, YoshimiMitsunori, MikiIn this research, a Genetic Algorithms framework for Running On Parallel environments, which is named GAROP, is proposed. The GAROP provides the library for a parallel processing, so that users should only describe codes for genetic algorithms (GA) programs, utilizing the library implemented for the part requiring a parallel processing. In the GAROP framework, GA research provides only program codes which are concerned with GA algorithm and GAROP library supports other codes which are concerned with parallel processing. The advantage of using GAROP is to increase the user's productivity by making it possible to develop the program, which can execute a parallel processing. In this paper, the broad description of the GAROP is provided, and the development of the GAROP, corresponding multi-core CPU and GPU environments, is described. The libraries are implemented with GA which finds quasi-optimum solutions using meta heuristics, and its productivity and its parallelism are evaluated. As a result, only adding four descriptions to the program, the acceleration of the processing speed is confirmed in both of the environments; 5.26 times speed-up on multi-core CPU, and 3.0 times speed-up on GPU.In this research, a Genetic Algorithms framework for Running On Parallel environments, which is named GAROP, is proposed. The GAROP provides the library for a parallel processing, so that users should only describe codes for genetic algorithms (GA) programs, utilizing the library implemented for the part requiring a parallel processing. In the GAROP framework, GA research provides only program codes which are concerned with GA algorithm and GAROP library supports other codes which are concerned with parallel processing. The advantage of using GAROP is to increase the user's productivity by making it possible to develop the program, which can execute a parallel processing. In this paper, the broad description of the GAROP is provided, and the development of the GAROP, corresponding multi-core CPU and GPU environments, is described. The libraries are implemented with GA which finds quasi-optimum solutions using meta heuristics, and its productivity and its parallelism are evaluated. As a result, only adding four descriptions to the program, the acceleration of the processing speed is confirmed in both of the environments; 5.26 times speed-up on multi-core CPU, and 3.0 times speed-up on GPU.AN10505667研究報告数理モデル化と問題解決(MPS)2012-MPS-895162012-07-092012-07-04