{"id":3567,"date":"2016-07-29T09:14:14","date_gmt":"2016-07-29T00:14:14","guid":{"rendered":"http:\/\/www.is.doshisha.ac.jp\/news\/?p=3567"},"modified":"2016-07-29T09:14:14","modified_gmt":"2016-07-29T00:14:14","slug":"%e3%80%90%e9%80%9f%e5%a0%b1%e3%80%91the-2016-ieee-congress-on-evolutionary-computation","status":"publish","type":"post","link":"https:\/\/is.doshisha.ac.jp\/news\/?p=3567","title":{"rendered":"\u3010\u901f\u5831\u3011The 2016 IEEE Congress on Evolutionary Computation"},"content":{"rendered":"<p>2016\/7\/24-2016\/7\/29\u3000\u306e\u65e5\u7a0b\u3067\u3000\u30d0\u30f3\u30af\u30fc\u30d0\u30fc\u30fb\u30ab\u30ca\u30c0\u3000\u3067\u958b\u50ac\u3055\u308c\u305f\u3000The 2016 IEEE Congress on Evolutionary Computation\u306b\u3066\u767a\u8868\u3057\u307e\u3057\u305f\u3002<\/p>\n<ul>\n<li>Functional brain network extraction using a genetic algorithm with a kick-out method<\/li>\n<li>\u539f\u7530\u572d, \u7530\u4e2d\u7f8e\u91cc, \u65e5\u548c\u609f, Heiner Zille,Sanaz Mostaghim, \u5ee3\u5b89\u77e5\u4e4b<\/li>\n<\/ul>\n<p><!--more--><br \/>\n<strong>\u5b66\u4f1a\u53c2\u52a0\u5831\u544a\u66f8<\/strong><\/p>\n<table>\n<tbody>\n<tr>\n<td width=\"147\"><strong>\u5831\u544a\u8005\u6c0f\u540d<\/strong><\/td>\n<td width=\"373\">\u539f\u7530\u572d<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u767a\u8868\u8ad6\u6587\u30bf\u30a4\u30c8\u30eb<\/strong><\/td>\n<td width=\"373\">\u30ad\u30c3\u30af\u30a2\u30a6\u30c8\u624b\u6cd5\u3092\u7528\u3044\u305f\u907a\u4f1d\u7684\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u306b\u3088\u308b\u8133\u6a5f\u80fd\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u62bd\u51fa<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u767a\u8868\u8ad6\u6587\u82f1\u30bf\u30a4\u30c8\u30eb<\/strong><\/td>\n<td width=\"373\">Functional brain network extraction using a genetic algorithm with a kick-out method<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u8457\u8005<\/strong><\/td>\n<td width=\"373\">\u539f\u7530\u572d, \u7530\u4e2d\u7f8e\u91cc, \u65e5\u548c\u609f, Heiner Zille,<br \/>\nSanaz Mostaghim, \u5ee3\u5b89\u77e5\u4e4b<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u4e3b\u50ac<\/strong><\/td>\n<td width=\"373\">IEEE WCCI<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u56fd\u969b\u5b66\u4f1a\u540d<\/strong><\/td>\n<td width=\"373\">The 2016 IEEE Congress on Evolutionary Computation<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u4f1a\u5834<\/strong><\/td>\n<td width=\"373\">Canada, Vancouver, The Vancouver Convention Centre<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u958b\u50ac\u65e5\u7a0b<\/strong><\/td>\n<td width=\"373\">2016\/7\/24-2016\/7\/29<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&nbsp;<br \/>\n&nbsp;<\/p>\n<ol>\n<li>\u56fd\u969b\u5b66\u4f1a\u306e\u8a73\u7d30<\/li>\n<\/ol>\n<p>The 2016 IEEE Congress on Evolutionary Computation (IEEE CEC 2016)\u304c2016\u5e747\u670824\u65e5\uff5e29\u65e5\u306b\u304b\u3051\u3066\u3001\u30ab\u30ca\u30c0\u306e\u30d0\u30f3\u30af\u30fc\u30d0\u30fc\u306b\u3042\u308bVancouver Convention Centre\u306b\u3066\u958b\u50ac\u3055\u308c\u307e\u3057\u305f\u3002\u672c\u7814\u7a76\u5ba4\u304b\u3089\u306f\u3001\u5ee3\u5b89\u5148\u751f\u3001\u539f\u7530\u572d\uff08M1\uff09\u306e2\u540d\u304c\u53c2\u52a0\u3057\u307e\u3057\u305f\u3002\u767a\u8868\u5f62\u5f0f\u306f\u3001\u30dd\u30b9\u30bf\u30fc\u767a\u8868\u3067\u3001\u300cFunctional Brain Network Extraction Using a Genetic Algorithm with a Kick-Out Method\u300d\u3068\u3044\u3046\u984c\u76ee\u3067\u5b66\u4f1a\u6700\u7d42\u65e5\u306e7\u670829\u65e5\u306e\u5348\u5f8c2\u6642\u304b\u30896\u6642\u306b\u304b\u3051\u3066\u767a\u8868\u81f4\u3057\u307e\u3057\u305f\u3002\u30dd\u30b9\u30bf\u30fc\u767a\u8868\u304c\u4ed6\u306e\u53e3\u982d\u767a\u8868\u3068\u4e26\u884c\u3057\u3066\u884c\u308f\u308c\u3066\u3044\u305f\u306b\u3082\u95a2\u308f\u3089\u305a\u3001\u591a\u304f\u306e\u65b9\u306b\u8db3\u3092\u904b\u3093\u3067\u3082\u3089\u3044\u3001\u8a71\u3092\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3057\u305f\u3002<br \/>\n\u79c1\u305f\u3061\u304c\u53c2\u52a0\u3057\u305fIEEE CEC 2016\u306f\u4e09\u3064\u306e\u5b66\u4f1a\u3068\u5408\u540c\u3067\u884c\u308f\u308c\u307e\u3057\u305f\u3002\u6b8b\u308a\u4e8c\u3064\u306e\u5b66\u4f1a\u306f\u3001The 2016 International Joint Conference on Neural Networks (IJCNN 2016)\u3068The 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2016)\u3067\u3059\u3002\u4eca\u56de\u306e\u56fd\u969b\u5b66\u4f1a\u3067\u306f\u3001IEEE World Congress on Computational Intelligence(WCCI)\u306e\u4e3b\u50ac\u306b\u3088\u3063\u3066\u3053\u306e\u4e09\u3064\u306e\u5b66\u4f1a\u304c\u958b\u50ac\u3055\u308c\u3001\u305d\u308c\u305e\u308c\u8074\u8b1b\u3059\u308b\u6a5f\u4f1a\u306b\u6075\u307e\u308c\u307e\u3057\u305f\u3002\u307e\u305f\u3001\u6ede\u5728\u3057\u305f\u671f\u9593\u3092\u901a\u3057\u3066\u30d0\u30f3\u30af\u30fc\u30d0\u30fc\u306f\u6674\u308c\u6e21\u308b\u5929\u5019\u3067\u3001\u4f11\u61a9\u306e\u5408\u9593\u306b\u306f\u3001\u4f1a\u5834\u304b\u3089\u7d20\u6674\u3089\u3057\u3044\u666f\u8272\u3001\u5c71\u3001\u6d77\u3001\u8239\u3092\u671b\u3080\u3053\u3068\u304c\u3067\u304d\u307e\u3057\u305f\u3002<br \/>\nIEEE CEC 2016\u3067\u306f\u3001optimization\u3092\u4e2d\u5fc3\u3068\u3057\u305fgenetic algorithm\u3084surrogate model\u306a\u3069\u306e\u9032\u5316\u8a08\u7b97\u5206\u91ce\u306e\u767a\u8868\u304c\u884c\u308f\u308c\u307e\u3057\u305f\u3002\u767a\u8868\u8005\u306e\u4e2d\u306b\u306f\u3001\u6628\u5e74\u306e12\u6708\u306b\u884c\u308f\u308c\u305f\u7b2c9\u56de\u9032\u5316\u8a08\u7b97\u30b7\u30f3\u30dd\u30b8\u30a6\u30e0\u306b\u53c2\u52a0\u3055\u308c\u3066\u3044\u305f\u751f\u5f92\u3084\u5148\u751f\u65b9\u3082\u304a\u308a\u3001\u518d\u4f1a\u3092\u679c\u305f\u3059\u3053\u3068\u304c\u3067\u304d\u307e\u3057\u305f\u3002\u307e\u305f\u3001IJCNN 2016\u306e\u767a\u8868\u3082\u3044\u304f\u3064\u304b\u8074\u8b1b\u3057\u3001deep learning \u3084support vector machine\u306e\u6700\u5148\u7aef\u306e\u7814\u7a76\u306b\u3082\u89e6\u308c\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3057\u305f\u3002\u3055\u3089\u306b\u3001IJCNN 2016\u3067\u306f\u3001MRI\u3084EEG\u3092\u306f\u3058\u3081\u3068\u3059\u308b\u8133\u60c5\u5831\u3092\u89e3\u6790\u3059\u308b\u7814\u7a76\u3082\u898b\u3089\u308c\u3001\u5927\u5909\u8208\u5473\u6df1\u304b\u3063\u305f\u3067\u3059\u3002\u6765\u5e74\u306f\u3001\u3053\u306e\u30bb\u30c3\u30b7\u30e7\u30f3\u306a\u3089\u3001\u672c\u7814\u7a76\u5ba4\u306e\u65b9\u3082\u4f55\u4eba\u304b\u53c2\u52a0\u3059\u308b\u3053\u3068\u306f\u53ef\u80fd\u3067\u306f\u306a\u3044\u304b\u3068\u611f\u3058\u307e\u3057\u305f\u3002<br \/>\nIEEE WCCI 2016 \u30db\u30fc\u30e0\u30da\u30fc\u30b8 http:\/\/www.wcci2016.org\/index.php<\/p>\n<ol start=\"2\">\n<li>\u7814\u7a76\u767a\u8868\n<ul>\n<li>\u767a\u8868\u6982\u8981<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n<p>\u79c1\u3068\u5ee3\u5b89\u5148\u751f\u306f\u300129\u65e5\u306e\u5348\u5f8c2\u6642\u304b\u30896\u6642\u306b\u304b\u3051\u3066\u306e\u30dd\u30b9\u30bf\u30fc\u767a\u8868\u306b\u53c2\u52a0\u81f4\u3057\u307e\u3057\u305f\u3002\u767a\u8868\u5f62\u5f0f\u306f\u30dd\u30b9\u30bf\u30fc\u5f62\u5f0f\u3067\u884c\u308f\u308c\u307e\u3057\u305f\u3002<br \/>\n\u4eca\u56de\u306e\u5b66\u4f1a\u767a\u8868\u3067\u306f\uff0c\u8133\u6a5f\u80fd\u306e\u91cd\u8981\u306a\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u306e\u62bd\u51fa\u6642\u9593\u306e\u9ad8\u901f\u5316\u3092\u76ee\u7684\u3068\u3057\u305fkick-out method\u3092\u63d0\u6848\u3044\u305f\u3057\u307e\u3057\u305f\u3002Kick-out method\u306f\u3001\u907a\u4f1d\u7684\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u306e\u4e00\u90e8\u306b\u5c0e\u5165\u3055\u308c\u3001\u5148\u884c\u7814\u7a76\u3088\u308a30%\u8fd1\u304f\u51e6\u7406\u6642\u9593\u3092\u524a\u6e1b\u3057\u3001\u5909\u308f\u3089\u306c\u6027\u80fd\u3092\u767a\u63ee\u3059\u308b\u3053\u3068\u304c\u793a\u5506\u3055\u308c\u307e\u3057\u305f\u30024\u6642\u9593\u3068\u3044\u3046\u9577\u671f\u306b\u308f\u305f\u308b\u30dd\u30b9\u30bf\u30fc\u30bb\u30c3\u30b7\u30e7\u30f3\u3067\u3057\u305f\u304c\u3001\u5e38\u306b\u8ab0\u304b\u304c\u30dd\u30b9\u30bf\u30fc\u3092\u898b\u306b\u304d\u3066\u304a\u308a\u3001\u591a\u304f\u306e\u3054\u610f\u898b\u3092\u9802\u304d\u307e\u3057\u305f\uff0e\u4ee5\u4e0b\u306b\u6284\u9332\u3092\u8a18\u8f09\u81f4\u3057\u307e\u3059\uff0e<\/p>\n<table>\n<tbody>\n<tr>\n<td width=\"529\">This paper proposed the method to reduce the calculating time to reveal the functional brain network associated with a task using a genetic algorithm and functional near-infrared spectroscopy (fNIRS) data. Changes in the cerebral blood flow during a task are obtained as time series data is analyzed using fNIRS, and a correlation matrix for multiple fNIRS channels is created for each subject. The subject group is divided into two groups, and a classifier of the two groups learns the correlation matrix as a feature quantity. The correlation matrix changes as the feature quantity changes with the combinations of channels, which affects classifier accuracy. If the combination of channels with the best classifier accuracy is identified, these channels can be considered important to the creation of the functional brain network for a target task. In our study, a genetic algorithm (GA) is used for channel selection. However, learning the classifier to calculate the evaluation value and optimization by the GA requires significant time. Thus, to increase search efficiency, we propose the kick-out method to skip the evaluation value calculation for poor individuals according to a previous evaluation value. We evaluated the effectiveness of the proposed method using fNIRS data recorded during a mental rotation test. Results show that important channels that express the functional brain network were selected and that processing time was reduced significantly by the proposed method.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&nbsp;<br \/>\n&nbsp;<br \/>\n&nbsp;<br \/>\n&nbsp;<br \/>\n&nbsp;<br \/>\n&nbsp;<br \/>\n&nbsp;<br \/>\n&nbsp;<\/p>\n<ul>\n<li>\u8cea\u7591\u5fdc\u7b54<\/li>\n<\/ul>\n<p>\u4eca\u56de\u306e\u8b1b\u6f14\u767a\u8868\u3067\u306f\uff0c\u4ee5\u4e0b\u306e\u3088\u3046\u306a\u8cea\u7591\u3092\u53d7\u3051\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<br \/>\n<strong>\u30fb\u8cea\u554f\u5185\u5bb9<\/strong><strong>1<\/strong><br \/>\n\u4e2d\u56fd\u306e\u5b66\u751f\u306e\u65b9\u304b\u3089\u306e\u8cea\u554f\u3092\u53d7\u3051\u307e\u3057\u305f\uff0e\u3053\u3061\u3089\u306e\u8cea\u554f\u306f\uff0c\u62bd\u51fa\u3055\u308c\u305f\u8133\u6a5f\u80fd\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u304c\u672c\u5f53\u306b\u91cd\u8981\u306a\u8133\u90e8\u4f4d\u306a\u306e\u304b\u3092\u691c\u8a3c\u3057\u305f\u304b\u3068\u3044\u3046\u3082\u306e\u3067\u3057\u305f\u3002\u3053\u306e\u8cea\u554f\u306b\u5bfe\u3059\u308b\u79c1\u306e\u56de\u7b54\u306f\uff0c\u540c\u3058\u8ab2\u984c\u3092\u6271\u3046\u5148\u884c\u7814\u7a76\u3068\u6bd4\u8f03\u3059\u308b\u3053\u3068\u3067\u691c\u8a0e\u3092\u884c\u3044\u3001\u5148\u884c\u7814\u7a76\u3067\u3044\u3046\u6d3b\u6027\u3057\u305f\u90e8\u5206\u3068\u3053\u3061\u3089\u304c\u62bd\u51fa\u3057\u305f\u91cd\u8981\u306a\u90e8\u5206\u3068\u306e\u8133\u90e8\u4f4d\u306e\u4f4d\u7f6e\u304c\u540c\u3058\u50be\u5411\u306b\u3042\u308b\u3068\u56de\u7b54\u3057\u307e\u3057\u305f\uff0e\u305d\u306e\u7b54\u3048\u306b\u5bfe\u3057\u3066\uff0c\u901a\u5e38\u306f\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u3092\u7528\u3044\u3066\u7d50\u679c\u3092\u691c\u8a0e\u3057\u3001\u305d\u306e\u7d50\u679c\u306b\u5fdc\u3058\u3066\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u306e\u518d\u4fee\u6b63\u3092\u884c\u3046\u3001\u3068\u3044\u3046\u9806\u5e8f\u3092\u7e70\u308a\u8fd4\u3059\u3082\u306e\u3060\u304b\u3089\u3001\u3084\u306f\u308a\u3082\u3046\u4e00\u5ea6\u7d50\u679c\u306e\u691c\u8a3c\u3092\u3057\u3063\u304b\u308a\u884c\u3063\u3066\u304f\u3060\u3055\u3044\u3068\u8a00\u308f\u308c\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<br \/>\n<strong>\u30fb\u8cea\u554f\u5185\u5bb9<\/strong><strong>2<\/strong><br \/>\n\u4e2d\u6771\u51fa\u8eab\u306e\u5148\u751f\u306b\u3054\u610f\u898b\u3092\u9802\u304d\u307e\u3057\u305f\uff0e\u73fe\u5728\u53d6\u308a\u7d44\u3093\u3067\u3044\u308b\u554f\u984c\u306e\u8a2d\u8a08\u5909\u6570\u306e\u6570\u304c22\u3067\u3001\u7d44\u307f\u5408\u308f\u305b\u304c\u7d04500\u4e07\u901a\u308a\u3042\u308b\u304c\u3001\u3053\u306e\u7d44\u307f\u5408\u308f\u305b\u6570\u306a\u3089\u308f\u3056\u308f\u3056\u6700\u9069\u5316\u3092\u4f7f\u3046\u5fc5\u8981\u304c\u306a\u304f\u3001\u5168\u63a2\u7d22\u3067\u3082\u5341\u5206\u306b\u63a2\u7d22\u304c\u53ef\u80fd\u306a\u306e\u3067\u306f\u306a\u3044\u304b\u3068\u3044\u3046\u3082\u306e\u3067\u3057\u305f\u3002\u3053\u306e\u8cea\u554f\u306b\u5bfe\u3057\u3066\uff0c\u4eca\u56de\u306f\u624b\u6cd5\u306e\u6709\u7528\u6027\u3092\u78ba\u8a8d\u3059\u308b\u305f\u3081\u306e\u30c6\u30b9\u30c8\u554f\u984c\u3067\u3042\u308a\u3001\u73fe\u572894\u30c1\u30e3\u30f3\u30cd\u30eb\u306e\u8133\u90e8\u4f4d\u304b\u3089\u91cd\u8981\u306a\u7d44\u307f\u5408\u308f\u305b\u3092\u691c\u8a0e\u3057\u3066\u3044\u308b\u3068\u7b54\u3048\u307e\u3057\u305f\uff0e\u3059\u308b\u3068\uff0c\u5f7c\u306f\u305d\u308c\u306a\u3089\u3070\u307e\u3063\u305f\u304f\u554f\u984c\u306f\u306a\u3044\u3068\u8a00\u308f\u308c\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<\/p>\n<ul>\n<li>\u611f\u60f3<\/li>\n<\/ul>\n<p>\u65e5\u672c\u4eba\u3001\u4e2d\u56fd\u4eba\u3001\u4e2d\u6771\u4eba\u3001\u30a2\u30e1\u30ea\u30ab\u4eba\u3001\u30a4\u30ae\u30ea\u30b9\u4eba\u3068\u6570\u591a\u304f\u306e\u56fd\u306e\u65b9\u3005\u306b\u8db3\u3092\u904b\u3093\u3067\u3044\u305f\u3060\u304d\u3001\u8208\u5473\u3092\u6301\u3063\u3066\u805e\u3044\u3066\u3044\u305f\u3060\u3051\u308b\u3053\u3068\u304c\u51fa\u6765\u307e\u3057\u305f\uff0e\u8cea\u7591\u5fdc\u7b54\u306b\u3082\u3001\u307b\u3068\u3093\u3069\u76f8\u624b\u306e\u610f\u56f3\u3092\u304f\u307f\u53d6\u3063\u3066\u7b54\u3048\u3089\u308c\u3066\u3044\u305f\u3068\u611f\u3058\u3066\u3044\u307e\u3059\u3002\u305f\u3060\u3057\u3001\u30a2\u30e1\u30ea\u30ab\u4eba\u306e\u304b\u305f\u306b\u53d7\u3051\u305f\u8cea\u554f\u3067\u3001\u51e6\u7406\u3092\u884c\u3063\u3066\u3044\u308b\u30c7\u30fc\u30bf\u306f\u3001\u672c\u5f53\u306b\u8ab2\u984c\u6642\u306e\u30c7\u30fc\u30bf\u3092\u62bd\u51fa\u3057\u3066\u3044\u308b\u306e\u304b\u3001\u3068\u3044\u3063\u305f\u53b3\u3057\u3044\u8cea\u554f\u3082\u3042\u308a\u3001\u4eca\u5f8c\u306e\u91cd\u8981\u306a\u691c\u8a0e\u8ab2\u984c\u3067\u3042\u308b\u3053\u3068\u3092\u8a8d\u8b58\u3044\u305f\u3057\u307e\u3057\u305f\uff0e\u81ea\u5206\u306e\u7814\u7a76\u3092\u76f8\u624b\u306b\u82f1\u8a9e\u3067\u4f1d\u3048\u308b\u3068\u3044\u3046\u7df4\u7fd2\u306f\u3082\u3061\u308d\u3093\u3001\u305d\u306e\u305f\u3081\u306b\u6539\u3081\u3066\u3001\u81ea\u3089\u306e\u7814\u7a76\u3092\u898b\u76f4\u3059\u826f\u3044\u6a5f\u4f1a\u3068\u306a\u308a\u307e\u3057\u305f\u3002<br \/>\n&nbsp;<br \/>\n&nbsp;<br \/>\n&nbsp;<br \/>\n&nbsp;<br \/>\n&nbsp;<br \/>\n&nbsp;<br \/>\n&nbsp;<br \/>\n&nbsp;<br \/>\n&nbsp;<br \/>\n&nbsp;<br \/>\n&nbsp;<\/p>\n<ol start=\"3\">\n<li>\u8074\u8b1b<\/li>\n<\/ol>\n<p>\u4eca\u56de\u306e\u56fd\u969b\u5b66\u4f1a\u3067\u306f\uff0c\u4e0b\u8a18\u306e6\u4ef6\u306e\u767a\u8868\u3092\u8074\u8b1b\u3057\u307e\u3057\u305f\uff0e<\/p>\n<table>\n<tbody>\n<tr>\n<td width=\"529\">\u767a\u8868\u30bf\u30a4\u30c8\u30eb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Evolving Polyomino Puzzles<br \/>\n\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Daniel Ashlock and Lauren Taylor<br \/>\n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Session MM-12 : Computational Intelligence and Games<br \/>\nAbstract : A polyomino puzzle is a collection of polyominos that can be joined to make a simple shape. The game Ten-Yen was one of the first of these. It has ten polyomino pieces that could be used to make a 6&#215;6 square in a variety of ways. In this study we define representations and fitness functions for generating polyomino<br \/>\npuzzles as well as developing a simple solver to compare the evolved puzzles. The solver can be used to approximate the number of solutions and hence the relative difficulty of the puzzles. Two types of fitness functions are compared, the second<br \/>\nof which was developed to deal with scaling issues that arose with the first. A parameter study on the algorithm is performed and it is found that simply penalizing bad results is more effective than parameter tuning. This study concludes by discussing potential puzzle variants.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u3053\u306e\u8074\u8b1b\u306f\u3001\u30bf\u30a4\u30c8\u30eb\u3092\u307f\u3066\u8208\u5473\u3092\u6301\u3063\u305f\u306e\u3067\u3001\u805e\u304d\u306b\u884c\u304d\u307e\u3057\u305f\u3002\u3059\u308b\u3068\u3001\u30d7\u30ec\u30bc\u30f3\u30c6\u30fc\u30bf\u30fc\u306e\u65b9\u304c\u3001\u8eab\u632f\u308a\u624b\u632f\u308a\u30b8\u30e7\u30fc\u30af\u3001\u6642\u306b\u306f\u4f1a\u5834\u5185\u306b\u8cea\u554f\u3068\u3001\u89b3\u5ba2\u3092\u5f15\u304d\u8fbc\u3080\u306e\u304c\u3068\u3066\u3082\u4e0a\u624b\u304b\u3063\u305f\u306e\u304c\u7b2c\u4e00\u5370\u8c61\u3067\u3059\u3002\u7814\u7a76\u5185\u5bb9\u306f\u3001\u30dd\u30ea\u30aa\u30df\u30ce\u30d1\u30ba\u30eb\u30676&#215;6\u306e\u6b63\u65b9\u5f62\u3092\u751f\u6210\u3059\u308b\u3001\u305d\u306e\u305f\u3081\u306b\u751f\u6210\u3059\u308b\u305f\u3081\u306e\u8868\u73fe\u3068\u9069\u5fdc\u5ea6\u95a2\u6570\u3092\u8003\u3048\u308b\u3068\u3044\u3046\u3082\u306e\u3067\u3057\u305f\u3002\u4eca\u56de\u306e\u5b66\u4f1a\u3067\u591a\u6570\u306e\u65b9\u304c\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u306e\u958b\u767a\u3092\u3057\u3066\u3044\u305f\u4e00\u65b9\u3067\u3001\u3053\u306e\u767a\u8868\u3067\u306f\u5bfe\u8c61\u554f\u984c\u306b\u7126\u70b9\u3092\u5f53\u3066\u3066\u3044\u3066\u3001\u4e00\u969b\u7570\u5f69\u3092\u653e\u3063\u3066\u3044\u305f\u3068\u540c\u6642\u306b\u3001\u65e5\u672c\u3067\u306f\u77e5\u308c\u306a\u3044\u8003\u3048\u65b9\u3092\u77e5\u308b\u6a5f\u4f1a\u3068\u306a\u3063\u3066\u3088\u304b\u3063\u305f\u3067\u3059\u3002<br \/>\n&nbsp;<\/p>\n<table>\n<tbody>\n<tr>\n<td width=\"529\">\u767a\u8868\u30bf\u30a4\u30c8\u30eb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1aEliteNSGA-III: An Improved Evolutionary Many-Objective Optimization Algorithm<br \/>\n\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Amin Ibrahim, Shahryar Rahnamayan, Miguel Vargas Martin and Kalyanmoy Deb<br \/>\n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Session TA-13 Multi-objective Evolutionary Algorithms<br \/>\nAbstract : Evolutionary algorithms are the most studied and successful population-based algorithms for solving single- and multi-objective optimization problems. However, many studies have shown that these algorithms fail to perform well when handling many-objective (more than three objectives) problems due to the loss of selection pressure to pull the population towards the Pareto front. As a result, there has been a number of efforts towards developing evolutionary algorithms that can successfully handle many-objective optimization problems without deteriorating the effect of evolutionary operators. A reference point based NSGA-II(NSGA-III) is one such algorithm designed to deal with many-objective problem, where the diversity of the solution is guided by a number of well-spread reference points. However, NSGA-III still has difficulty preserving elite population as new solutions are generated. In this paper, we proposed as improved NSGA-III algorithm, called EliteNSGA-III to improve the diversity and accuracy of the NSGA-III algorithm. EliteNSGA-III algorithm maintains an elite population archive to Preserve previously generated elite solutions that would probably be eliminated by NSGA-III\u2019s selection procedure. The proposed EliteNSGA-III algorithm is applied to 11 many-objective test problems with three to 15 objectives. Experimental results show that the proposed EliteNSGA-III algorithm outperforms the obtained solutions, especially for test problems with higher objectives.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u307e\u305a\u3001\u79c1\u304c\u4eca\u73fe\u5728\u4f7f\u7528\u3057\u3066\u3044\u308bNSGA-II\u306b\u5bfe\u3057\u3066\u3001NSGA-III\u306e\u5b58\u5728\u3092\u30bf\u30a4\u30c8\u30eb\u3092\u898b\u3066\u521d\u3081\u3066\u77e5\u308a\u307e\u3057\u305f\u3002\u3055\u3089\u306b\u305d\u306e\u30a8\u30ea\u30fc\u30c8\u9078\u629e\u306e\u6bb5\u968e\u3092\u5411\u4e0a\u3055\u305b\u305f\u306e\u304c\u3053\u3061\u3089\u306e\u7814\u7a76\u3067\u3059\u3002\u767a\u8868\u3067\u306f\u3001\u500b\u4f53\u306e\u591a\u69d8\u6027\u306e\u7dad\u6301\u3092\u4e09\u6b21\u5143\u306e\u30b0\u30e9\u30d5\u3084\u56f3\u3067\u3001\u5f93\u6765\u306eNSGA-III\u3068\u6bd4\u8f03\u3057\u3066\u304a\u308a\u3001\u3053\u308c\u304b\u3089\u79c1\u3082\u591a\u69d8\u6027\u306e\u7dad\u6301\u3092\u8003\u3048\u306a\u3051\u308c\u3070\u306a\u3089\u306a\u3044\u306e\u3067\u3001\u53c2\u8003\u306b\u306a\u308a\u307e\u3057\u305f\u3002\u5f7c\u3089\u306f\u6700\u7d42\u4e16\u4ee3\u306e\u307f\u306e\u500b\u4f53\u72b6\u6cc1\u3092\u79c1\u305f\u3061\u306b\u898b\u305b\u3066\u3044\u307e\u3057\u305f\u304c\u3001\u79c1\u306f\u305d\u308c\u307e\u3067\u306e\u4e16\u4ee3\u3059\u3079\u3066\u306b\u304a\u3051\u308b\u591a\u69d8\u6027\u306e\u7dad\u6301\u3092\u56f3\u3067\u78ba\u8a8d\u3057\u305f\u304b\u3063\u305f\u3067\u3059\u3002\u3084\u306f\u308a\u3001\u591a\u76ee\u7684\u6700\u9069\u5316\u3092\u8003\u3048\u308b\u3046\u3048\u3067\u3001\u591a\u69d8\u6027\u306e\u7dad\u6301\u3092\u4fdd\u3064\u3053\u3068\u306f\u3053\u308c\u304b\u3089\u306e\u9818\u57df\u3060\u3068\u518d\u78ba\u8a8d\u3057\u307e\u3057\u305f\u3002<br \/>\n&nbsp;<\/p>\n<table>\n<tbody>\n<tr>\n<td width=\"529\">\u767a\u8868\u30bf\u30a4\u30c8\u30eb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a How to Compare Many-Objective Algorithms under Different Settings of Population and Archive Sizes<br \/>\n\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Hisao Ishibuchi, Yu Setoguchi, Hiroyuki Masuda and Yusuke Nojima<br \/>\n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Session TA-16 : Many-Objective Optimization<br \/>\nAbstract : In the evolutionary multi-objective optimization community, algorithm comparison is usually performed under the same population size. However, this is not always fair because its best specification is usually different in each algorithm. In many-objective optimization, the number of solutions to be found may depend on the situation. If the decision maker wants to analyze the entire Pareto front, thousands of solutions may be needed. If the decision maker wants to choose a single final solution from some candidates after their quick checks, only a\u3000small number of representative solutions may be needed. In this paper, we discuss how to evaluate the ability of evolutionary many-objective optimization algorithms to find an arbitrarily specified number of non-dominated solutions. Our idea is the use\u3000of solution selection after the termination of each algorithm. We examine two\u3000scenarios: One is solution selection from the final population, and the other is from all of the examined solutions. Through computational experiments, first we\u3000demonstrate that performance comparison heavily depends on the population size.<br \/>\nThen we examine the effects of solution selection from the final population and the examined solutions on comparison results.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u3053\u306e\u7814\u7a76\u306f\u3001\u591a\u76ee\u7684\u6700\u9069\u5316\u3092\u4f7f\u7528\u3059\u308b\u4e0a\u3067\u3001\u6bcd\u96c6\u56e3\u306e\u5927\u304d\u3055\u3068\u30a2\u30fc\u30ab\u30a4\u30d6\u306e\u5927\u304d\u3055\u306b\u3064\u3044\u3066\u6bd4\u8f03\u3068\u691c\u8a0e\u3092\u3057\u3066\u3044\u307e\u3057\u305f\u3002\u767a\u8868\u3067\u306f\u30013\u76ee\u7684\u306e\u6642\u306e\u591a\u76ee\u7684\u6700\u9069\u5316\u3092\u9664\u304f\u3001\u591a\u304f\u306e\u30d9\u30f3\u30c1\u30de\u30fc\u30af\u554f\u984c\u306b\u5bfe\u3057\u3066\u3001\u6bcd\u96c6\u56e3\u6570\u304c\u591a\u304f\u306a\u308b\u307b\u3069\u3001MOEA\/D\u304c\u52b9\u679c\u7684\u3067\u3042\u308b\u3053\u3068\u3092\u56f3\u3068\u8868\u3092\u7528\u3044\u3066\u8aac\u660e\u3057\u3066\u3044\u307e\u3057\u305f\u3002\u5f7c\u3089\u306f\u3001MOEA\/D\u3001NSGA-III\u3001MOEA\/D\/D\u3001\u03b8-DEA\u306e\u56db\u3064\u3092\u6bd4\u8f03\u3057\u3066\u3044\u307e\u3057\u305f\u304c\u3001\u305d\u3082\u305d\u3082\u79c1\u306b\u305d\u306e\u56db\u3064\u3068\u3082\u306e\u77e5\u8b58\u304c\u306a\u304f\u3001\u307e\u3060\u307e\u3060\u3053\u308c\u304b\u3089\u52c9\u5f37\u304c\u5fc5\u8981\u3060\u3068\u6539\u3081\u3066\u611f\u3058\u308b\u3053\u3068\u3068\u306a\u308a\u307e\u3057\u305f\u3002<br \/>\n&nbsp;<\/p>\n<table>\n<tbody>\n<tr>\n<td width=\"529\">\u767a\u8868\u30bf\u30a4\u30c8\u30eb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1aAccelerating Evolutionary Computation Using Estimated Convergence Points<br \/>\n\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Jun Yu, Yan Pei and Hideyuki Takagi<br \/>\n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Session TM-12 Numerical Optimization<br \/>\nAbstract : We use the convergence points estimated by our proposed method as elite individuals for evolutionary computation and evaluate the acceleration effect and analyze the effect and computational cost. The worst individuals in population are replaced with the convergence points estimated from the moving<br \/>\nvectors between parent individuals and their offspring; i.e. these convergence points are used as elite individuals. Differential evolution (DE) and 14 benchmark functions are used in our evaluation experiments. The experimental results show that use of the estimated convergence points as elite can accelerate DE<br \/>\nsearch in spite of the calculation cost of the convergence points. We finally analyze the components of the proposed estimation method to improve cost-performance.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u3053\u306e\u7814\u7a76\u3067\u306f\u3001Differential evaluation\u3092\u5229\u7528\u3057\u3001\u3042\u3089\u304b\u3058\u3081\u30d9\u30f3\u30c1\u30de\u30fc\u30af\u554f\u984c\u306e\u89e3\u306e\u53ce\u675f\u70b9\u3092\u30d9\u30af\u30c8\u30eb\u3092\u7528\u3044\u3066\u63a8\u6e2c\u3059\u308b\u3053\u3068\u3067\u3001\u51e6\u7406\u6642\u9593\u306e\u9ad8\u901f\u5316\u3092\u56f3\u308b\u3082\u306e\u3067\u3057\u305f\u3002\u65b9\u6cd5\u306f\u9055\u3048\u3069\u3001\u89e3\u3092\u4e88\u6e2c\u3057\u3066\u9ad8\u901f\u5316\u3092\u56f3\u308b\u3068\u3044\u3046\u70b9\u3067\u306f\u3001\u4eca\u5b66\u4f1a\u3067\u79c1\u304c\u767a\u8868\u3068\u91cd\u306a\u308b\u306e\u3067\u3001\u5927\u5909\u8208\u5473\u3092\u6301\u3063\u3066\u8074\u8b1b\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3057\u305f\u3002\u307e\u305f\u3001\u691c\u8a0e\u6bd4\u8f03\u306b\u304a\u3044\u3066\u3001\u56db\u3064\u306e\u5411\u4e0a\u3092\u898b\u8fbc\u3093\u3060\u624b\u6cd5\u3092\u63d0\u6848\u3057\u3066\u304a\u308a\u3001\u5b9f\u9a13\u7d50\u679c\u3088\u308a\u63d0\u6848\u624b\u6cd5\u306e1\u30012\u30014\u3092\u7d44\u307f\u5408\u308f\u305b\u308b\u306e\u304c\u52b9\u679c\u7684\u3067\u3042\u308b\u3068\u7d50\u8ad6\u4ed8\u3051\u3066\u3044\u307e\u3057\u305f\u3002\u3053\u306e\u3088\u3046\u306a\u6bd4\u8f03\u691c\u8a0e\u306e\u4ed5\u65b9\u3001\u767a\u8868\u306e\u4ed5\u65b9\u306f\u4eca\u5f8c\u306e\u53c2\u8003\u306b\u3057\u3088\u3046\u3068\u601d\u3044\u307e\u3057\u305f\u3002<br \/>\n&nbsp;<br \/>\n&nbsp;<\/p>\n<table>\n<tbody>\n<tr>\n<td width=\"529\">\u767a\u8868\u30bf\u30a4\u30c8\u30eb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1aMulti-objective Variable Subset Selection Using Heterogeneous Surrogate Modeling and Sequential Design<br \/>\n\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Joachim van der Herten, Ivo Couckuyt, Dirk Deschrijver and Tom Dhaene<br \/>\n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Session TM-16 Many-Objective Optimization<br \/>\nAbstract : Constructing surrogate models of high-dimensional complex black-box systems from simulation-based data requires an appropriate choice of surrogate model type, as well as identification of the most influential input parameters. As including irrelevant input parameters results in a longer surrogate model training process and potentially increases the risk of overfitting, it is important to identify a small set of relevant parameters during the adaptive modeling phase of the surrogate modeling process. A multi-objective optimization step is proposed to identify both the appropriate model type as well as a parameters subset. The obtained model can be used for evaluation intensive applications such as exploration, sensitivity analysis or optimization.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u3053\u306e\u7814\u7a76\u306f\u3001\u30b5\u30ed\u30b2\u30fc\u30c8\u30e2\u30c7\u30eb\u3068\u30b5\u30dd\u30fc\u30c8\u30d9\u30af\u30bf\u30fc\u30de\u30b7\u30f3\u3092\u4f7f\u7528\u3057\u3066\u304a\u308a\u3001\u79c1\u306e\u7814\u7a76\u3068\u4f3c\u3066\u3044\u308b\u306e\u3067\u306f\u306a\u3044\u304b\u3068\u601d\u3044\u8074\u8b1b\u306b\u884c\u304d\u307e\u3057\u305f\u3002\u5f7c\u3089\u306f\u3001\u30b5\u30ed\u30b2\u30fc\u30c8\u30e2\u30c7\u30eb\u306e\u7a2e\u985e\u306b\u3064\u3044\u3066\u8ad6\u3058\u3066\u304a\u308a\u7279\u306b\u3001\u3088\u308a\u30b7\u30f3\u30d7\u30eb\u3067\u52b9\u679c\u7684\u306a\u3082\u306e\u3001\u3088\u308a\u8a13\u7df4\u6642\u9593\u304c\u77ed\u3044\u3082\u306e\u3001\u305d\u3057\u3066\u66f4\u65b0\u3055\u308c\u308b\u4e16\u4ee3\u306e\u4e2d\u3067\u30aa\u30fc\u30d0\u30fc\u30d5\u30a3\u30c3\u30c6\u30a3\u30f3\u30b0\u306e\u5371\u967a\u6027\u3092\u4f4e\u6e1b\u3067\u304d\u308b\u3082\u306e\u306e\u4e09\u3064\u306b\u7126\u70b9\u3092\u5f53\u3066\u3066\u3044\u307e\u3057\u305f\u3002\u5f7c\u3089\u306f\u3069\u306e\u30b5\u30ed\u30b2\u30fc\u30c8\u30e2\u30c7\u30eb\u3092\u4f7f\u3046\u304b\u3001\u3069\u308c\u3060\u3051\u306e\u8a55\u4fa1\u70b9\u6570\u304c\u3088\u3044\u304b\u3092\u4e2d\u5fc3\u306b\u7d50\u8ad6\u4ed8\u3051\u3066\u304a\u308a\u3001\u79c1\u3082\u4eca\u5f8c\u691c\u8a0e\u3057\u306a\u3051\u308c\u3070\u3044\u3051\u306a\u3044\u9818\u57df\u3067\u3042\u308b\u3053\u3068\u3092\u518d\u78ba\u8a8d\u3057\u307e\u3057\u305f\u3002<br \/>\n&nbsp;<\/p>\n<table>\n<tbody>\n<tr>\n<td width=\"529\">\u767a\u8868\u30bf\u30a4\u30c8\u30eb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1aNovel Crossover and Mutation Operation in Genetic Algorithm for Clustering<br \/>\n\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a A. H. Beg and Md Zahidul Islam<br \/>\n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Session WA-13 Genetic Algorithms<br \/>\nAbstract : In this paper we propose a Genetic Algorithm based clustering technique called GMC that produces high quality chromosomes in the initial population. The proposed technique also introduces two phases of crossover operation<br \/>\nwith extensive chromosomes generation aiming to produce high-quality offspring chromosomes and prevent degeneracy. The proposed technique also introduces three steps of mutation operation in order to improve chromosome quality.<br \/>\nGMC uses a probabilistic selection approach in order to gradually improve the chromosomes quality of a population. We compare the proposed technique GMC with five existing techniques on 10 publicly available data sets in terms of two<br \/>\nwell-known evaluation criteria: Silhouette Coefficient and DB Index. Our experimental results demonstrate statistically significant superiority of GMC over the existing techniques, and the effectiveness of the proposed components.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u3053\u306e\u7814\u7a76\u3067\u306f\u3001\u30af\u30e9\u30b9\u30bf\u30ea\u30f3\u30b0\u306b\u57fa\u3065\u3044\u305f\u907a\u4f1d\u7684\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u306e\u63d0\u6848\u3092\u3057\u3066\u304a\u308a\u3001\u521d\u671f\u306e\u6bcd\u96c6\u56e3\u751f\u6210\u306b\u304a\u3051\u308b\u9ad8\u3044\u8cea\u306e\u907a\u4f1d\u5b50\u3092\u4f5c\u6210\u3067\u304d\u308b\u3068\u8ff0\u3079\u3066\u3044\u308b\u3002\u307e\u305f\u3001\u4ea4\u5dee\u65b9\u6cd5\u3067\u3082\u65b0\u305f\u306b\u4e8c\u7a2e\u985e\u63d0\u6848\u3057\u3066\u304a\u308a\u3001\u5927\u5909\u8208\u5473\u3092\u62b1\u3044\u305f\u306e\u3067\u8074\u8b1b\u306b\u884c\u304d\u307e\u3057\u305f\u3002\u30af\u30e9\u30b9\u30bf\u30ea\u30f3\u30b0\u6280\u8853\u304c\u907a\u4f1d\u7684\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u306e\u521d\u671f\u751f\u6210\u306b\u52b9\u679c\u7684\u3060\u3068\u3059\u308b\u8ad6\u6587\u304c\u3042\u308b\u4e00\u65b9\u3067\u3001k-means\u6cd5\u306b\u306f\u30af\u30e9\u30b9\u30bf\u30fc\u6570\u306e\u5b9a\u7fa9\u306b\u554f\u984c\u304c\u3042\u308b\u305f\u3081\u3001\u5f7c\u3089\u306f\u3042\u3089\u304b\u3058\u3081\u7528\u610f\u3055\u308c\u305f\u6570\u5b57\u306e\u30bb\u30c3\u30c8\u304b\u3089\u7591\u4f3c\u30e9\u30f3\u30c0\u30e0\u7684\u306b\u9078\u629e\u3059\u308b\u65b9\u6cd5\u3092\u63d0\u6848\u3057\u305f\u3002\u79c1\u3082\u907a\u4f1d\u7684\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u306e\u521d\u671f\u751f\u6210\u306b\u306f\u691c\u8a0e\u306e\u5fc5\u8981\u304c\u3042\u308b\u3068\u5927\u304d\u304f\u611f\u3058\u3066\u3044\u308b\u306e\u3067\u3001\u4e00\u3064\u306e\u53c2\u8003\u8ad6\u6587\u3068\u3057\u3066\u899a\u3048\u3066\u304a\u304d\u305f\u3044\u3067\u3059\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"<p>2016\/7\/24-2016\/7\/29\u3000\u306e\u65e5\u7a0b\u3067\u3000\u30d0\u30f3\u30af\u30fc\u30d0\u30fc\u30fb\u30ab\u30ca\u30c0\u3000\u3067\u958b\u50ac\u3055\u308c\u305f\u3000The 2016 IEEE Congress on Evolutionary Computation\u306b\u3066\u767a\u8868\u3057\u307e\u3057\u305f\u3002 Functio &hellip; <\/p>\n<p class=\"link-more\"><a href=\"https:\/\/is.doshisha.ac.jp\/news\/?p=3567\" class=\"more-link\"><span class=\"screen-reader-text\">&#8220;\u3010\u901f\u5831\u3011The 2016 IEEE Congress on Evolutionary Computation&#8221; \u306e<\/span>\u7d9a\u304d\u3092\u8aad\u3080<\/a><\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[6],"tags":[],"class_list":["post-3567","post","type-post","status-publish","format-standard","hentry","category-6"],"_links":{"self":[{"href":"https:\/\/is.doshisha.ac.jp\/news\/index.php?rest_route=\/wp\/v2\/posts\/3567","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/is.doshisha.ac.jp\/news\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/is.doshisha.ac.jp\/news\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/is.doshisha.ac.jp\/news\/index.php?rest_route=\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/is.doshisha.ac.jp\/news\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=3567"}],"version-history":[{"count":0,"href":"https:\/\/is.doshisha.ac.jp\/news\/index.php?rest_route=\/wp\/v2\/posts\/3567\/revisions"}],"wp:attachment":[{"href":"https:\/\/is.doshisha.ac.jp\/news\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=3567"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/is.doshisha.ac.jp\/news\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=3567"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/is.doshisha.ac.jp\/news\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=3567"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}