Hi Stephen,

Thanks for the paper.  I have some colleagues that study deceptive energy 
landscapes but it is a different literature.

[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).]

Marcus

From: Friam <friam-boun...@redfish.com> on behalf of Stephen Guerin 
<stephen.gue...@simtable.com>
Reply-To: The Friday Morning Applied Complexity Coffee Group <friam@redfish.com>
Date: Wednesday, January 2, 2019 at 9:36 AM
To: The Friday Morning Applied Complexity Coffee Group <friam@redfish.com>
Subject: Re: [FRIAM] on selection pressure

Very cool, Marcus!

Did you interact with Ken Stanley 
(https://scholar.google.se/citations?user=6Q6oO1MAAAAJ&hl=en&oi=ao) 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.

In particular this paper:
  https://www.mitpressjournals.org/doi/pdfplus/10.1162/EVCO_a_00025

Also, Ken's homepage:
  http://www.cs.ucf.edu/~kstanley/ with more popular book links and Santa Fe 
Radio Cafe Interviews.

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:
  https://www.cs.utexas.edu/users/risto/
  http://nn.cs.utexas.edu/pub-list.php

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On Wed, Jan 2, 2019 at 9:11 AM Marcus Daniels 
<mar...@snoutfarm.com<mailto:mar...@snoutfarm.com>> wrote:
Some memory, and the ongoing recombination and optimization of less fit (high 
energy) individuals which tend to create other less fit individuals.
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.

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.)

On 1/2/19, 8:57 AM, "Friam on behalf of ∄ uǝʃƃ" 
<friam-boun...@redfish.com<mailto:friam-boun...@redfish.com> on behalf of 
geprope...@gmail.com<mailto:geprope...@gmail.com>> wrote:

    Are there computational (or otherwise not shown) costs to the members that 
continue in the free case but are pruned in the selection case?

    On 1/2/19 7:44 AM, Marcus Daniels wrote:
    > Here are a couple of plots from a large constrained optimization problem 
I've been running.
    > In the first case, I apply selection pressure:  If a solution is not in 
the top 200 performers, it dies.
    > In the second case, the population can continue to grow without concern 
for its performance.
    > 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).
    >
    > 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.


    --
    ∄ uǝʃƃ

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