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	<title>遺伝的アルゴリズム Archives &#8212; MATLAB Number ONE</title>
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	<title>遺伝的アルゴリズム 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>
		<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>
					<comments>https://matlab1.com/shop/matlab-code/train-bayesian-neural-network-genetic-algorithm-ga/#respond</comments>
		
		<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>Site layout planning Genetic Algorithm</title>
		<link>https://matlab1.com/shop/matlab-code/site-layout-planning-genetic-algorithm/</link>
					<comments>https://matlab1.com/shop/matlab-code/site-layout-planning-genetic-algorithm/#respond</comments>
		
		<dc:creator><![CDATA[global MATLAB]]></dc:creator>
		<pubDate>Sat, 27 Aug 2016 06:57:21 +0000</pubDate>
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					<description><![CDATA[<p>We considered the following facilities in MATLAB code : Facilities represented by a two word. These selected words are arbitrary. The user can add any facility to the code. There is no limitation to the number of facilities. In addition to, the user can consider a radius for a facility. This parameter is selected based [&#8230;]</p>
<p>The post <a href="https://matlab1.com/shop/matlab-code/site-layout-planning-genetic-algorithm/">Site layout planning Genetic Algorithm</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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