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<p class="MsoNormal">Hi Stephen,<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">Thanks for the paper. I have some colleagues that study deceptive energy landscapes but it is a different literature.<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">[I do like trying to figure out “How the hell did that work?!” more than a workman-like construction project. Maybe up to a point when the experiments and reverse engineering just get to be too hard (biology).]<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">Marcus<o:p></o:p></p>
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<p class="MsoNormal"><b><span style="font-size:12.0pt;color:black">From: </span></b><span style="font-size:12.0pt;color:black">Friam <friam-bounces@redfish.com> on behalf of Stephen Guerin <stephen.guerin@simtable.com><br>
<b>Reply-To: </b>The Friday Morning Applied Complexity Coffee Group <friam@redfish.com><br>
<b>Date: </b>Wednesday, January 2, 2019 at 9:36 AM<br>
<b>To: </b>The Friday Morning Applied Complexity Coffee Group <friam@redfish.com><br>
<b>Subject: </b>Re: [FRIAM] on selection pressure<o:p></o:p></span></p>
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<p class="MsoNormal">Very cool, Marcus! <o:p></o:p></p>
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<p class="MsoNormal"><o:p> </o:p></p>
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<p class="MsoNormal">Did you interact with Ken Stanley (<a href="https://scholar.google.se/citations?user=6Q6oO1MAAAAJ&hl=en&oi=ao" target="_blank">https://scholar.google.se/citations?user=6Q6oO1MAAAAJ&hl=en&oi=ao</a>) when he was at SFI a couple years back?
Ken's research would support your observations on the importance on the pressure to maintain novelty/diversity in evolutionary algorithms vs the focus on the objective function.<o:p></o:p></p>
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<p class="MsoNormal"><o:p> </o:p></p>
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<p class="MsoNormal">In particular this paper:<o:p></o:p></p>
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<p class="MsoNormal"> <a href="https://www.mitpressjournals.org/doi/pdfplus/10.1162/EVCO_a_00025" target="_blank">https://www.mitpressjournals.org/doi/pdfplus/10.1162/EVCO_a_00025</a><o:p></o:p></p>
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<p class="MsoNormal"><o:p> </o:p></p>
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<p class="MsoNormal">Also, Ken's homepage:<br>
<a href="http://www.cs.ucf.edu/~kstanley/" target="_blank">http://www.cs.ucf.edu/~kstanley/</a> with more popular book links and Santa Fe Radio Cafe Interviews.<o:p></o:p></p>
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<p class="MsoNormal"><o:p> </o:p></p>
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<p class="MsoNormal">BTW, in the late 90's I was working a bit on evolving weights and topologies of neural networks and was very inspired by Ken's advisor, Risto Miikkulainen, and his team at UT Austin:<o:p></o:p></p>
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<p class="MsoNormal"> <a href="https://www.cs.utexas.edu/users/risto/" target="_blank">https://www.cs.utexas.edu/users/risto/</a><o:p></o:p></p>
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<p class="MsoNormal"> <a href="http://nn.cs.utexas.edu/pub-list.php" target="_blank">http://nn.cs.utexas.edu/pub-list.php</a><o:p></o:p></p>
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<p class="MsoNormal">_______________________________________________________________________<br>
<a href="mailto:stephen.guerin@simtable.com" target="_blank">Stephen.Guerin@Simtable.com</a>
<o:p></o:p></p>
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<p class="MsoNormal">CEO, Simtable <a href="http://www.simtable.com/" target="_blank">http://www.simtable.com</a><o:p></o:p></p>
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<p class="MsoNormal">1600 Lena St #D1, Santa Fe, NM 87505 <o:p></o:p></p>
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<p class="MsoNormal">office: (505)995-0206 <span style="font-size:9.5pt">mobile: (505)577-5828</span><o:p></o:p></p>
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<p class="MsoNormal"><span style="font-size:9.5pt">twitter: @simtable</span><o:p></o:p></p>
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<p class="MsoNormal"><o:p> </o:p></p>
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<p class="MsoNormal">On Wed, Jan 2, 2019 at 9:11 AM Marcus Daniels <<a href="mailto:marcus@snoutfarm.com" target="_blank">marcus@snoutfarm.com</a>> wrote:<o:p></o:p></p>
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<p class="MsoNormal">Some memory, and the ongoing recombination and optimization of less fit (high energy) individuals which tend to create other less fit individuals. <br>
In this optimization system there are numerous methods that are used to create fit individuals, but the ones that create the very best individuals do not arise from recombination + selection pressure. Mixing two distinct (large Hamming distance) globally
constraint-satisfying solutions tends to create a non-constraint satisfying solutions. It is only once the two parents are very similar (e.g. same species) that such a recombination will even work, but by then it doesn't do all that much. <br>
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Computationally, it easier to try more approaches and maintain a large population than it is accelerate the algorithms that are most effective. (For the former, just add more cores.)<br>
<br>
On 1/2/19, 8:57 AM, "Friam on behalf of <span style="font-family:"Cambria Math",serif">
∄</span> uǝʃƃ" <<a href="mailto:friam-bounces@redfish.com" target="_blank">friam-bounces@redfish.com</a> on behalf of
<a href="mailto:gepropella@gmail.com" target="_blank">gepropella@gmail.com</a>> wrote:<br>
<br>
Are there computational (or otherwise not shown) costs to the members that continue in the free case but are pruned in the selection case?<br>
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On 1/2/19 7:44 AM, Marcus Daniels wrote:<br>
> Here are a couple of plots from a large constrained optimization problem I've been running. <br>
> In the first case, I apply selection pressure: If a solution is not in the top 200 performers, it dies.<br>
> In the second case, the population can continue to grow without concern for its performance. <br>
> This is a 5900-dimensional pseudo-boolean problem and the best-known solution is around 2.61e+08. Note the low end of the y axis is not close to this. In both cases, aggressive efforts are made to diversify the population and in both cases every shown
solution is unique (even though their energies can collide). <br>
> <br>
> In this case, I would argue that selection pressure has accomplished nothing -- conservatism doesn't work if the goal is to create the most fit individuals. The mean moves, if you care about that. But the very best solutions are nearly the same, and
neither have come close to the optimal. <br>
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<br>
-- <br>
<span style="font-family:"Cambria Math",serif">∄</span> uǝʃƃ<br>
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