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	<title>Comments on: SpeedyGA  v1.2.1</title>
	<atom:link href="http://blog.hackingevolution.net/2008/12/31/speedyga-v121/feed/" rel="self" type="application/rss+xml" />
	<link>http://blog.hackingevolution.net/2008/12/31/speedyga-v121/</link>
	<description>Explaning Adaptation in Evolutionary Systems</description>
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		<title>By: Keki</title>
		<link>http://blog.hackingevolution.net/2008/12/31/speedyga-v121/#comment-248</link>
		<dc:creator><![CDATA[Keki]]></dc:creator>
		<pubDate>Tue, 20 Jan 2009 02:18:42 +0000</pubDate>
		<guid isPermaLink="false">http://blog.hackingevolution.net/?p=519#comment-248</guid>
		<description><![CDATA[Hi Ramo,

Thanks for your kind comments. 

maskReposFactor is indeed a confusing name for a variable. Essentially, SpeedyGA pre-generates two &quot;repositories&quot; of random binary digits from which it picks the masks used in crossover and mutation operations (Since SpeedyGA is not generating these masks on the fly it saves time; random numbers are costly to generate.) maskReposFactor determines the size of these repositories. 

Depending on how it is interpretted, your question---what exactly is speedyGA doing?---is either a relatively simple question, or a very difficult one. 

SpeedyGA is just an implementation of a Simple Genetic Algorithm (SGA). For a description of the SGA, see Melanie Mitchell&#039;s book on genetic algorithms. If you&#039;re interested in how I&#039;ve *implemented* the SGA in Matlab, please read the comments in the code. If you&#039;re new to the idea of vectorization, there&#039;s some excellent online documentation to bring you up to speed. See http://www.mathworks.com/support/tech-notes/1100/1109.html .) 

On the other hand, if you&#039;re asking *why* the SGA works as well as it does when it is applied to hard combinatorial optimization problems, then you&#039;re asking a question which does not currently have a good answer. Answering this question has been the singular aim of my research over the past three years. I&#039;m currently writing up a theory which, I hope, will be well received by other researchers interested in this question. 

I&#039;m afraid I don&#039;t know enough about your problem to make a recommendation about a good way to apply an SGA to it. 

Hope this helps.]]></description>
		<content:encoded><![CDATA[<p>Hi Ramo,</p>
<p>Thanks for your kind comments. </p>
<p>maskReposFactor is indeed a confusing name for a variable. Essentially, SpeedyGA pre-generates two &#8220;repositories&#8221; of random binary digits from which it picks the masks used in crossover and mutation operations (Since SpeedyGA is not generating these masks on the fly it saves time; random numbers are costly to generate.) maskReposFactor determines the size of these repositories. </p>
<p>Depending on how it is interpretted, your question&#8212;what exactly is speedyGA doing?&#8212;is either a relatively simple question, or a very difficult one. </p>
<p>SpeedyGA is just an implementation of a Simple Genetic Algorithm (SGA). For a description of the SGA, see Melanie Mitchell&#8217;s book on genetic algorithms. If you&#8217;re interested in how I&#8217;ve *implemented* the SGA in Matlab, please read the comments in the code. If you&#8217;re new to the idea of vectorization, there&#8217;s some excellent online documentation to bring you up to speed. See <a href="http://www.mathworks.com/support/tech-notes/1100/1109.html" rel="nofollow">http://www.mathworks.com/support/tech-notes/1100/1109.html</a> .) </p>
<p>On the other hand, if you&#8217;re asking *why* the SGA works as well as it does when it is applied to hard combinatorial optimization problems, then you&#8217;re asking a question which does not currently have a good answer. Answering this question has been the singular aim of my research over the past three years. I&#8217;m currently writing up a theory which, I hope, will be well received by other researchers interested in this question. </p>
<p>I&#8217;m afraid I don&#8217;t know enough about your problem to make a recommendation about a good way to apply an SGA to it. </p>
<p>Hope this helps.</p>
]]></content:encoded>
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	<item>
		<title>By: Ramo</title>
		<link>http://blog.hackingevolution.net/2008/12/31/speedyga-v121/#comment-240</link>
		<dc:creator><![CDATA[Ramo]]></dc:creator>
		<pubDate>Fri, 16 Jan 2009 23:34:15 +0000</pubDate>
		<guid isPermaLink="false">http://blog.hackingevolution.net/?p=519#comment-240</guid>
		<description><![CDATA[Hi Keki,

I congratulate you on you SpeedyGA, it is very useful and quick !

I was wondering if you would be able to provide a detailed overview
of what exactly it is doing and how? and may be explain the reason behind
variables such as maxResposFactor and others?

Also, would you recommend it for time-series clustering (i.e. clustering different companies&#039; stock performance across time; where the fitness function is the degree of correlation within a cluster)?

what is the best way to implement it?

Your help in this matter is greatly appreciated,,
Thanks,]]></description>
		<content:encoded><![CDATA[<p>Hi Keki,</p>
<p>I congratulate you on you SpeedyGA, it is very useful and quick !</p>
<p>I was wondering if you would be able to provide a detailed overview<br />
of what exactly it is doing and how? and may be explain the reason behind<br />
variables such as maxResposFactor and others?</p>
<p>Also, would you recommend it for time-series clustering (i.e. clustering different companies&#8217; stock performance across time; where the fitness function is the degree of correlation within a cluster)?</p>
<p>what is the best way to implement it?</p>
<p>Your help in this matter is greatly appreciated,,<br />
Thanks,</p>
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