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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Natural selection. V. How to read the fundamental equations of evolutionary change in terms of information theory · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1211.4037/</link><description>Collected HN comments and submissions linking this paper.</description><language>en</language><lastBuildDate>Mon, 02 Nov 2026 11:16:22 GMT</lastBuildDate><atom:link href="https://hackerpaper.kaipfister.de/p/1211.4037/feed.xml" rel="self" type="application/rss+xml" /><item><title>Natural selection. V. How to read the fundamental equations of evolutionary change in terms of information theory — mentioned by Dn_Ab</title><link>https://news.ycombinator.com/item?id=8687922</link><guid isPermaLink="false">urn:hackerpapers:mention:1211.4037:8687922</guid><pubDate>Tue, 02 Dec 2014 16:34:14 GMT</pubDate><description>&lt;p&gt;In other settings, a simple approximation of what Evolution does will balance your portfolio and find Nash Equilibriums for zero sum games much more quickly than linear programming (query: multiplicative update evolution). Having to wait for things to die can be restrictive (but genomes are declarative, what does that buy?). Roughly, with what amounts to the assumption that Lamarckian evolution is more or less not true, you can characterize evolution&amp;#x27;s learning power as almost as good as PAC (query: statistical query learning evolution). See alsos…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1211.4037/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>q-bio.PE</category></item></channel></rss>
