[FRIAM] on selection pressure
Stephen Guerin
stephen.guerin at simtable.com
Wed Jan 2 11:51:30 EST 2019
Here's Ken's TV interview in Santa Fe which provides a nice review of the
findings in his book.
http://reportfromsantafe.com/episodes/view/293/ken-stanley-author-why-greatness-cannot-be-planned-the-myth-of-the-objective/
_______________________________________________________________________
Stephen.Guerin at Simtable.com <stephen.guerin at simtable.com>
CEO, Simtable http://www.simtable.com
1600 Lena St #D1, Santa Fe, NM 87505
office: (505)995-0206 mobile: (505)577-5828
twitter: @simtable
On Wed, Jan 2, 2019 at 9:49 AM Marcus Daniels <marcus at snoutfarm.com> wrote:
> 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-bounces at redfish.com> on behalf of Stephen Guerin <
> stephen.guerin at simtable.com>
> *Reply-To: *The Friday Morning Applied Complexity Coffee Group <
> friam at redfish.com>
> *Date: *Wednesday, January 2, 2019 at 9:36 AM
> *To: *The Friday Morning Applied Complexity Coffee Group <
> friam at 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
>
>
> _______________________________________________________________________
> Stephen.Guerin at Simtable.com <stephen.guerin at simtable.com>
>
> CEO, Simtable http://www.simtable.com
>
> 1600 Lena St #D1, Santa Fe, NM 87505
>
> office: (505)995-0206 mobile: (505)577-5828
>
> twitter: @simtable
>
>
>
>
>
> On Wed, Jan 2, 2019 at 9:11 AM Marcus Daniels <marcus at 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-bounces at redfish.com
> on behalf of gepropella at 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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