{"id":1530,"date":"2012-05-20T18:40:29","date_gmt":"2012-05-20T09:40:29","guid":{"rendered":"http:\/\/www.is.doshisha.ac.jp\/~tomo\/ftw\/?p=1530"},"modified":"2012-05-20T18:40:29","modified_gmt":"2012-05-20T09:40:29","slug":"1530","status":"publish","type":"post","link":"https:\/\/is.doshisha.ac.jp\/~tomo\/ftw\/?p=1530","title":{"rendered":"\u4e26\u5217\u907a\u4f1d\u7684\u30d7\u30ed\u30b0\u30e9\u30df\u30f3\u30b0"},"content":{"rendered":"

\u907a\u4f1d\u7684\u30d7\u30ed\u30b0\u30e9\u30df\u30f3\u30b0<\/strong><\/p>\n

\u907a\u4f1d\u7684\u30d7\u30ed\u30b0\u30e9\u30df\u30f3\u30b0\uff08Genetic Programming:GP)\u306f\uff0c1992\u5e74\u306bStanford\u5927\u5b66\u306eJohn Koza\u3089\u306b\u3088\u308a\u63d0\u6848\u3055\u308c\u305f\u9032\u5316\u8ad6\u7684\u8a08\u7b97\u624b\u6cd5\u3067\u3042\u308b\uff0eGP\u306f\uff0c\u907a\u4f1d\u7684\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\uff08Genetic Algorithms:GA)\u306e\u907a\u4f1d\u5b50\u578b\u3092\u69cb\u9020\u7684\u306a\u8868\u73fe\uff08\u6728\u69cb\u9020\uff0c\u30b0\u30e9\u30d5\u69cb\u9020\uff09\u304c\u6271\u3048\u308b\u3088\u3046\u306b\u62e1\u5f35\u3057\u305f\u3082\u306e\u3067\u3042\u308b\uff0eGP\u306f\u30d7\u30ed\u30b0\u30e9\u30e0\u751f\u6210\u3084\u5b66\u7fd2\uff0c\u63a8\u8ad6\uff0c\u6982\u5ff5\u5f62\u6210\u306a\u3069\u306b\u5fdc\u7528\u3059\u308b\u3053\u3068\u3092\u76ee\u6307\u3057\u3066\u3044\u308b\uff0e\u5177\u4f53\u7684\u306b\u306f\uff0c\u30d7\u30ed\u30b0\u30e9\u30e0\u3092\u9032\u5316\u3055\u305b\u3066\u76ee\u7684\u3068\u3059\u308b\u30ed\u30dc\u30c3\u30c8\u306e\u5236\u5fa1\u3084\u69cb\u9020\u7269\u306e\u8a2d\u8a08\u3092\u8a66\u307f\u3066\u3044\u308b\uff0e<\/p><\/blockquote>\n

http:\/\/is.doshisha.ac.jp\/isreport\/entry\/312<\/a>
\n\u4e26\u5217\u51e6\u7406<\/strong>
\nDavid Andre and John R. Koza. A parallel implementation of genetic programming that achieves super-linear performance. Information Sciences: an International Journal, 106:201\u2013218, 1998.
\nGP\u306e\u4e26\u5217\u51e6\u7406\u3001\u5cf6\u30e2\u30c7\u30eb
\nD. Robilliard, V. Marion, and C. Fonlupt. High performance genetic
\nprogramming on GPU. In Proceedings of the 2009 workshop on Bioinspired
\nalgorithms for distributed systems, pages 85\u201394, Barcelona,
\nSpain, 2009. ACM, New York.
\n\u30de\u30b9\u30bf\u30fc\u30b9\u30ec\u30fc\u30d6\u30e2\u30c7\u30eb\u3000\uff27\uff30\uff35
\n\uff27\uff30\uff35\u3092\u5229\u7528\u3057\u305f\u4e26\u5217\u51e6\u7406\u306f\u5c71\u306e\u306e\u3088\u3046\u306b\u3042\u308b
\nDarren M. Chitty, A data parallel approach to genetic programming using programmable graphics hardware, Proceeding GECCO ’07 Proceedings of the 9th annual conference on Genetic and evolutionary computation Pages 1566-1573 , ACM New York, NY, USA \u00a92007
\n\uff27\uff30\uff35\u3092\u5229\u7528\u3057\u305f\u4e26\u5217\u51e6\u7406, \u30c7\u30fc\u30bf\u51e6\u7406
\nD. Robilliard, V. Marison-Poty, and C. Fonlupt, Populaiton parallel GP on the g80 GPU, In Proceedings of the 11th European Conference on Gentic Programming. Springer, 2008
\nM. Oussaid`ene, B. Chopard, O.V. Pictel, and M. Tomassini, \u201cParallel genetic programming\u3000and itsa pplication to trading model induction\u201d, Parallel Computing 23 (1997),\u30001183\u20131198.
\nMaster Slave model
\nGarnett Wilson, Wolfgang Banzhaf,Deployment of CPU and GPU-based genetic programming on heterogeneous devices, Proceeding GECCO ’09 Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers Pages 2531-2538, ACM New York, NY, USA \u00a92009
\nArpit A. Almal, et. al., Using genetic programming to classify node positive patients in bladder cancer, Proceeding GECCO ’06 Proceedings of the 8th annual conference on Genetic and evolutionary computation Pages 239-246 ACM New York, NY, USA \u00a92006
\n\u30dd\u30a4\u30f3\u30c8<\/strong><\/p>\n