文件列表:

matlab_neat (0, 2003-09-01)
matlab_neat\initial_population.m (3675, 2003-05-20)
matlab_neat\neat_main.m (15156, 2003-08-22)
matlab_neat\rep.m (1162, 1994-04-18)
matlab_neat\reproduce.m (30437, 2003-08-19)
matlab_neat\xor_experiment.m (4904, 2003-08-22)
matlab_neat\xor_experiment_vec.m (4136, 2003-08-22)

This distribution (v1.0, 8/31/03) contains a Matlab implementation of the NeuroEvolution of Augmenting Topologies (NEAT) method for evolving neural network topologies and weights. The package includes an implementation of the XOR experiment as an example. -The complete source code in this distribution was written by Christian Mayr, who can be reached at: matlab_neat@web.de -The NEAT method was developed by Kenneth Stanley and Risto Miikkulainen. Additional information and papers describing NEAT in detail can be found at Ken's website: http://www.cs.utexas.edu/users/kstanley/ A FAQ for Matlab NEAT may also become available through: http://www.cs.utexas.edu/users/kstanley/neat.html This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License version 2 as published by the Free Software Foundation. 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. We hope that this software will be a useful starting point for your own explorations in neuroevolution. The software is provided as is, however, we will do our best to maintain it and accommodate suggestions. If you want to be notified of future releases of the software or have questions, comments, bug reports or suggestions related to the software or source code itself, send email to matlab_neat@web.de. For general questions about NEAT, send e-mail to kstanley@cs.utexas.edu, or refer to the NEAT User's Page and FAQ at http://www.cs.utexas.edu/users/kstanley/neat.html. NOTES: -To run: After including the directory in which you installed matlab neat in your startup.m file, just type "neat_main" at the Matlab command line. The XOR experiment will run and graphs will be displayed to indicate progress. -Data Output: At the end of evolution, all population structures are in memory. For example, to access the connection genes of population member 12, you type at the prompt population(12).connectiongenes. The entire population is stored in the population structure. Matlab NEAT also outputs its structures in the file "neatsave.mat," which can be loaded in using "load 'neatsave'" to restore data from a prior run. Please note that .mat files saved from Matlab NEAT will only be usable on the same platform on which they were saved, since they are saved in a binary format. -Modifying the code: It should be possible to modify the code and add experiments by using the extensive commenting as an aid in understanding the system. -XOR Termination Criterion: XOR is considered solved if the rounded outputs are all correct. In other words, anything below 0.5 is round down to 0, and anything equal or above 0.5 is rounded to 1. In the case the problem is solved, Matlab NEAT automatically assigns a fitness of 16 in order to terminate. -Vectorized XOR: Since the vectorized xor evaluation is about 3-5 times faster (especially with large, densely connected networks), users should use this version as a template. It may look a little strange (at least for users not familiar with matlab), but the speed increase justifies a little more coding effort. Those more comfortable with the familiar can, of course, use the old (looped) version.

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