Popular recipes tagged "genetic_algorithm"http://code.activestate.com/recipes/tags/genetic_algorithm/2012-10-02T16:18:36-07:00ActiveState Code RecipesArtificial Neuroglial Network (ANGN) (Python) 2012-10-02T16:18:36-07:00David Adlerhttp://code.activestate.com/recipes/users/4182015/http://code.activestate.com/recipes/578242-artificial-neuroglial-network-angn/ <p style="color: grey"> Python recipe 578242 by <a href="/recipes/users/4182015/">David Adler</a> (<a href="/recipes/tags/artificial_intelligence/">artificial_intelligence</a>, <a href="/recipes/tags/genetic_algorithm/">genetic_algorithm</a>, <a href="/recipes/tags/genetic_algorithms/">genetic_algorithms</a>, <a href="/recipes/tags/neural/">neural</a>, <a href="/recipes/tags/neural_networks/">neural_networks</a>). Revision 5. </p> <p>This is an attempt at emulating the algorithm from these scientific articles:</p> <ol> <li><a href="http://www.hindawi.com/journals/cmmm/2012/476324/">2011 - Artificial Astrocytes Improve Neural Network Performance</a></li> <li><a href="http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0019109">2012 - Computational Models of Neuron-Astrocyte Interactions Lead to Improved Efficacy in the Performance of Neural Networks</a></li> </ol> <p>The objective of the program is to train a neural network to classify the four inputs (the dimensions of a flower) into one of three categories (three species of flower), (taken from the <a href="http://archive.ics.uci.edu/ml/datasets/Iris">Iris Data Set</a> from the UCI Machine Learning Repository). This program has two learning phases: the first is a genetic algorithm (supervised), the second is a neuroglial algorithm (unsupervised). This ANGN is a development of a previous program only consisting of a genetic algorithm which can be found <a href="http://code.activestate.com/recipes/578241-genetic-algorithm-neural-network-in-python-source-/">here</a>.</p> <p>The second phase aims to emulate astrocytic interaction with neurons in the brain. The algorithm is based on two axioms: a) astrocytes are activated by persistent neuronal activity b) astrocytic effects occur over a longer time-scale than neurons. Each neuron has an associated astrocyte which counts the number of times its associated neuron fires (+1 for active -1 for inactive). If the counter reaches its threshold (defined as <code>Athresh</code>) the astrocyte is activated and for the next x iterations (defined as <code>Adur</code>) the astrocyte modifies the incoming weights to that particular neuron. If the counter reached a maximum due to persistent firing the incoming weights are increase by 25% for the proceeding <code>Adur</code> iterations; conversely if the counter reached a minimum due to persistent lack of firing the weights are decreased by 50% for the following <code>Adur</code> iterations). For a detailed description of the algorithm see the linked articles. For a general understanding of how this program was coded look at the pseudo-code/schematic <a href="http://commons.wikimedia.org/wiki/File:ANGN_schematic.png">here</a>.</p> <p>Any comments for improvements are welcome. There are several issues in this program which require addressing, please scroll down below code to read about these issues.</p> Evolutionary Algorithm (Generation of Prime Numbers) (Python) 2011-11-27T06:45:00-08:00Alexander James Wallarhttp://code.activestate.com/recipes/users/4179768/http://code.activestate.com/recipes/577964-evolutionary-algorithm-generation-of-prime-numbers/ <p style="color: grey"> Python recipe 577964 by <a href="/recipes/users/4179768/">Alexander James Wallar</a> (<a href="/recipes/tags/algorithm/">algorithm</a>, <a href="/recipes/tags/example/">example</a>, <a href="/recipes/tags/genetic/">genetic</a>, <a href="/recipes/tags/genetic_algorithm/">genetic_algorithm</a>, <a href="/recipes/tags/genetic_algorithms/">genetic_algorithms</a>, <a href="/recipes/tags/list/">list</a>, <a href="/recipes/tags/number/">number</a>, <a href="/recipes/tags/of/">of</a>, <a href="/recipes/tags/prime/">prime</a>, <a href="/recipes/tags/primelist/">primelist</a>, <a href="/recipes/tags/primes/">primes</a>, <a href="/recipes/tags/theory/">theory</a>). </p> <p>This is an evolutionary algorithm that returns a random list of prime numbers. This code is highly inefficient for a reason. This algorithm is more of a proof of concept that if a prime was a heritable trait, it would not be a desired one. </p> <p>Parameters:</p> <p>isPrime --> n: number to check if it is prime allPrimes --> n: size of list of random primes, m: the primes in the list will be between 0 and m</p> Evolution optimization strategy (Python) 2009-04-20T06:31:13-07:00Agnius Vasiliauskashttp://code.activestate.com/recipes/users/4169134/http://code.activestate.com/recipes/576645-evolution-optimization-strategy/ <p style="color: grey"> Python recipe 576645 by <a href="/recipes/users/4169134/">Agnius Vasiliauskas</a> (<a href="/recipes/tags/ai/">ai</a>, <a href="/recipes/tags/evolution/">evolution</a>, <a href="/recipes/tags/evolution_strategy/">evolution_strategy</a>, <a href="/recipes/tags/gene/">gene</a>, <a href="/recipes/tags/genetic_algorithm/">genetic_algorithm</a>, <a href="/recipes/tags/np_complete/">np_complete</a>, <a href="/recipes/tags/optimization/">optimization</a>). Revision 6. </p> <p>Algorithm for solving search and optimization problems.</p>