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	<title>genetic algorithm Archives &#8212; MATLAB Number ONE</title>
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	<title>genetic algorithm Archives &#8212; MATLAB Number ONE</title>
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		<title>Genetic Algorithms for web page classification</title>
		<link>https://matlab1.com/shop/matlab-code/genetic-algorithms-web-page-classification/</link>
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		<dc:creator><![CDATA[global MATLAB]]></dc:creator>
		<pubDate>Thu, 08 Sep 2016 13:38:03 +0000</pubDate>
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					<description><![CDATA[<p>The genetic algorithm calculated the weights wij for each term. Initially I assigned random real valued weights (between 0 and 1). The GA computed the best weights using the training dataset. Then I classified each test page by computing the cosine similarity of the vector (weights) learned by the GA with the normalized tf vector of the [&#8230;]</p>
<p>The post <a href="https://matlab1.com/shop/matlab-code/genetic-algorithms-web-page-classification/">Genetic Algorithms for web page classification</a> appeared first on <a href="https://matlab1.com">MATLAB Number ONE</a>.</p>
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		<title>Genetic Algorithms in Dynamic Environments</title>
		<link>https://matlab1.com/shop/matlab-code/genetic-algorithms-dynamic-environments/</link>
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		<dc:creator><![CDATA[global MATLAB]]></dc:creator>
		<pubDate>Thu, 08 Sep 2016 13:09:50 +0000</pubDate>
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					<description><![CDATA[<p>Dynamic optimization problems are a kind of optimization problems that involve changes over time. They pose a serious challenge to traditional optimization methods as well as conventional genetic algorithms since the goal is no longer to search for the optimal solution(s) of a fixed problem but to track the moving optimum over time. Dynamic optimization problems have attracted [&#8230;]</p>
<p>The post <a href="https://matlab1.com/shop/matlab-code/genetic-algorithms-dynamic-environments/">Genetic Algorithms in Dynamic Environments</a> appeared first on <a href="https://matlab1.com">MATLAB Number ONE</a>.</p>
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		<title>Multi-Layer Perception Neural Network training by Genetic Algorithm</title>
		<link>https://matlab1.com/shop/matlab-code/multi-layer-perception-neural-network-training-genetic-algorithm/</link>
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		<dc:creator><![CDATA[global MATLAB]]></dc:creator>
		<pubDate>Thu, 08 Sep 2016 10:47:41 +0000</pubDate>
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					<description><![CDATA[<p>The ability to learn and generalize is fundamental to any learning machine. In particular, generalization behavior is one of the most important topics in any classifier trained non-parametrically (e.g., neural networks). Artificial neural networks (ANNs) use inductive learning to find general concepts from their concrete examples. If there is set of input output to pairs called training set, the network parameters [&#8230;]</p>
<p>The post <a href="https://matlab1.com/shop/matlab-code/multi-layer-perception-neural-network-training-genetic-algorithm/">Multi-Layer Perception Neural Network training by Genetic Algorithm</a> appeared first on <a href="https://matlab1.com">MATLAB Number ONE</a>.</p>
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		<title>Multicast routing with bandwidth and delay constraints based on genetic algorithms</title>
		<link>https://matlab1.com/shop/matlab-code/multicast-routing-bandwidth-delay-constraints-based-genetic-algorithms/</link>
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		<dc:creator><![CDATA[global MATLAB]]></dc:creator>
		<pubDate>Wed, 07 Sep 2016 14:13:21 +0000</pubDate>
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					<description><![CDATA[<p>MATLAB Code of the following paper is ready for download. reference paper : Younes, Ahmed. &#8220;Multicast routing with bandwidth and delay constraints based on genetic algorithms.&#8221; Egyptian Informatics Journal 12.2 (2011): 107-114. Abstract : Many multimedia communication applications require a source to send multimedia information to multiple destinations through a communication network. To support these applications, it [&#8230;]</p>
<p>The post <a href="https://matlab1.com/shop/matlab-code/multicast-routing-bandwidth-delay-constraints-based-genetic-algorithms/">Multicast routing with bandwidth and delay constraints based on genetic algorithms</a> appeared first on <a href="https://matlab1.com">MATLAB Number ONE</a>.</p>
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		<title>Train Bayesian neural network by Genetic Algorithm (GA)</title>
		<link>https://matlab1.com/shop/matlab-code/train-bayesian-neural-network-genetic-algorithm-ga/</link>
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		<dc:creator><![CDATA[global MATLAB]]></dc:creator>
		<pubDate>Sun, 28 Aug 2016 13:50:54 +0000</pubDate>
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					<description><![CDATA[<p>In this MATLAB code, Bayesian Neural Network is trained by Genetic Algorithm. reference : Ji, Junzhong, et al. &#8220;A hybrid method for learning Bayesian networks based on ant colony optimization.&#8221; Applied Soft Computing 11.4 (2011): 3373-3384. &#160; Train Bayesian neural network by Particle swarm optimization (PSO) &#160; Train Bayesian neural network by Ant Colony Optimization [&#8230;]</p>
<p>The post <a href="https://matlab1.com/shop/matlab-code/train-bayesian-neural-network-genetic-algorithm-ga/">Train Bayesian neural network by Genetic Algorithm (GA)</a> appeared first on <a href="https://matlab1.com">MATLAB Number ONE</a>.</p>
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		<title>Memetic Algorithm MATLAB code</title>
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		<dc:creator><![CDATA[global MATLAB]]></dc:creator>
		<pubDate>Fri, 25 Sep 2015 07:12:52 +0000</pubDate>
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					<description><![CDATA[<p>The term ‘Memetic Algorithms’ (MAs) was introduced in the late 80s to denote a family of metaheuristics that have as central theme the hybridization of different algorithmic approaches for a given problem. Special emphasis was given to the use of a population-based approach in which a set of cooperating and competing agents were engaged in [&#8230;]</p>
<p>The post <a href="https://matlab1.com/shop/matlab-code/memetic-algorithm-matlab-code/">Memetic Algorithm MATLAB code</a> appeared first on <a href="https://matlab1.com">MATLAB Number ONE</a>.</p>
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