This program is distributed in the hope that it will be useful, ** but WITHOUT ANY WARRANTY without even the implied warranty of ** MERCHANTABILITY or fitness FOR A PARTICULAR PURPOSE. See the ** GNU General Public License for more details.
標簽： distributed WARRANTY program WITHOUT
上傳時間： 2016-01-11
上傳用戶：thesk123
/* This a simple genetic algorithm implementation where the */ /* evaluation function takes positive values only and the */ /* fitness of an individual is the same as the value of the */ /* objective function
標簽： implementation evaluation algorithm function
上傳時間： 2016-01-18
上傳用戶：wkchong
// Copyright (c), Philips Semiconductors Gratkorn // (C)PHILIPS Electronics N.V.2000 // All rights are reserved. // Philips reserves the right to make changes without notice at any time. // Philips makes no warranty, expressed, implied or statutory, including but // not limited to any implied warranty of merchantibility or fitness for any //particular purpose, or that the use will not infringe any third party patent, // copyright or trademark. Philips must not be liable for any loss or damage // arising from its use.
標簽： Semiconductors Electronics Copyright Gratkorn
上傳時間： 2016-02-04
上傳用戶：xuanjie
THIS DESIGN IS PROVIDED TO YOU "AS IS". XILINX MAKES AND YOU RECEIVE NO WARRANTIES OR CONDITIONS, EXPRESS, IMPLIED, STATUTORY OR OTHERWISE, AND XILINX SPECIFICALLY DISCLAIMS ANY IMPLIED WARRANTIES OF MERCHANTABILITY, NON-INFRINGEMENT, OR fitness FOR A PARTICULAR PURPOSE. This design has not been verified on hardware (as opposed to simulations), and it should be used only as an example design, not as a fully functional core. XILINX does not warrant the performance, functionality, or operation of this Design will meet your requirements, or that the operation of the Design will be uninterrupted or error free, or that defects in the Design will be corrected. Furthermore, XILINX does not warrant or make any representations regarding use or the results of the use of the Design in terms of correctness, accuracy, reliability or otherwise.
標簽： CONDITIONS WARRANTIES YOU PROVIDED
上傳時間： 2016-03-21
上傳用戶：1427796291
實現了一個簡單的花朵進化的模擬過程。 花朵的種群數量是10，共進化了50代。 通過運行程序，你會發現通過不斷的進化，種群的總的適應環境的能力在逐步提高（fitness的值下降）。
上傳時間： 2013-12-20
上傳用戶：縹緲
A dissipative particle swarm optimization is developed according to the self-organization of dissipative structure. The negative entropy is introduced to construct an opening dissipative system that is far-from-equilibrium so as to driving the irreversible evolution process with better fitness. The testing of two multimodal functions indicates it improves the performance effectively. structure. The negative entropy is introduced to construct an opening dissipative system that is far-from-equilibrium so as to driving the irreversible evolution process with better fitness. The testing of two multimodal functions indicates it improves the performance effectively.
標簽： self-organization optimization dissipative developed
上傳時間： 2016-03-31
上傳用戶：zgu489
基于多線程機制的，利用Matlab編寫，粒子群優化算法。目標變量采用歸一化處理，適用于所有的優化函數。優化函數自定義為fitness(x)。
上傳時間： 2013-12-30
上傳用戶：banyou
個程序就是最基本的粒子群優化算法程序，用Matlab實現，非常簡單。是主函數的源程序，優化函數則以m文件的形式放在fitness.m里面，對不同的優化函數只要修改fitness.m就可以了通用性很強。
上傳時間： 2013-12-05
上傳用戶：franktu
小波神經網絡的源程序: 1.構造的非線性函數: 位于nninit_test.m 2.直接用WNN逼近非線性:Wnn_test.m, (內部調用小波函數) 3.遺傳算法優化后逼近 :GA_Wnn_test.m (內部調用遺傳算法的,初始化,適應度,解碼函數)-genetic algorithm optimization WNN source : 1. Construction of the nonlinear function : nninit_test.m at 2. WNN directly with nonlinear approximation : Wnn_test.m. (internal called wavelet function) 3. Genetic Algorithm optimization approach : GA_Wnn_test.m (internal called genetic algorithms, initialize, fitness and decoding functions)
標簽： nninit_test GA_Wnn_tes Wnn_test WNN
上傳時間： 2016-09-17
上傳用戶：LIKE
This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY without even the implied warranty of * MERCHANTABILITY or fitness FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details.
標簽： distributed WARRANTY program WITHOUT
上傳時間： 2013-12-02
上傳用戶：star_in_rain
