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	<title>आनुवंशिक एल्गोरिथ्म Archives &#8212; MATLAB Number ONE</title>
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	<title>आनुवंशिक एल्गोरिथ्म Archives &#8212; MATLAB Number ONE</title>
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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>Multicast routing with bandwidth and delay constraints based on genetic algorithms</title>
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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>
		<link>https://matlab1.com/shop/matlab-code/memetic-algorithm-matlab-code/</link>
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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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