In attendence\u00a0: Lee, Kyle, Tom, Emma, Kwaku, Omri<\/p>\n
three fundamental features of biological evolution (from abstract)
\n1) particulate genes carry some subtle consequences for biological evolution that have not yet translated mainstream EC
\n2) the adaptive properties of the genetic code illustrate how both communities can contribute to a common understanding of appropriate evolutionary abstractions
\n3) EC exploration of representational language and its role in the genotype\/phenotype debate<\/p>\n
Regarding #3 — consider some of the points in the Language Instinct. We don’t want language to change, even though the meaning change – better said, we don’t want a faithful transcription of sound via characters because these aren’t robust to change. think about the mit article I forwarded – these loop thingies aren’t calculating exact solutions, but approximate solutions. Relationship between GP\/GA and other stochastic learning systems (and probabilistic systems) and approximation algorithms.<\/p>\n
can approximation algorithms make seemingly inherently sequential problems into parallel problems?<\/p>\n
“conscious intelligence vs natural selection”
\ni.e. engineering vs science
\ni.e. optimal solutions vs approximate solutions<\/p>\n
randomness not just of the RV but of the model?
\nmeta-randomness
\nsecond-order randomness
\nprobabilistic programming languages as second order randomness?
\nneed padding in randomness, redundancy to ensure robustness<\/p>\n
buidling a system that is sound and consistent but not complete – but GPs are already turing complete<\/p>\n
need probabilistic analysis in the test cases – starting with concise representations of the test cases’ domains – think like an ML – domains are sets that can be represented in closed form or with a generating function<\/p>\n
what is the purpose of having EC mimic biology?
\n(1) to make adaptive programs, following the intuition that evolution is a powerful force we need to come to understand
\n(2) to better understand biology and complex stochastic systems<\/p>\n
this paper references Ostrowski and Reynolds as people who are studying EC from a search perspective<\/p>\n
(1) PARTICULATE GENES AND POPULATION GENETICS
\n(2) THE ADAPTIVE GENETIC CODE
\ntom’s thoughts:<\/p>\n
(3) THE DICHOTOMY OF GENOTYPE AND PHENOTYPE<\/p>\n
genetic drift as a desirable characteristic<\/p>\n
authors posit that particulate genes will help redefine recombination. i am still curious about the role of redundancy in EC and whether it has been investigated – connections to language, to probabilistic language classes<\/p>\n
my angle: redundancy – it’s everywhere! doesn’t conflict with the bayesian stuff either<\/p>\n
how to balance small populations, where mutations are more pronounced, with the dilution that occurs over larger populations over many generations – maintaining locality (and perhaps a linearity of fitness) while viewing a population-wide (ie across subpopulations) concentration of parameters\/measure\/etc.<\/p>\n
IMPORTANT: “Unless offspring are infinite in number, their allele frequencies will not accurately mirror those of the parental generation, but instead will show some sampling error (genetic drift).”<\/p>\n
“In effect, pariculate genes in finite populations improve the evolutionary heuristic from a simple hill climbing algorithm to something closer to simulated annealing under a fluctuating temperature.”<\/p>\n
“co-adapted gene complexes” – Fisher, 1930 and O’Reilly 1999<\/p>\n
Adaptive cookbook = REDUNDANCY<\/p>\n
error minimizing code smoothing the fintess landscape? – comes from upublished data<\/p>\n
lee’s stuff
\ntag space machine
\nworks with stacks, all code lives in the tag space
\nratio tags, denseness of numbers
\ncucumber – got the R spec and cucumber book
\nsmaller GP steps into cycle
\nsome red\/green thing?
\n1) penumbra
\n2) keep cool? – climate change negotiation game between countries – can we find or quickly write a simulator?
\n3) Zeek Nieremberg – new lab member – filing Div 3, mostly a natural science guy
\n4) physical space, course partcipation – GP and intro class on creativity<\/p>\n
tom stuff
\n1) size-based nodes for tournie selection – turn into journal article? tom’s not that interested, but it is low-hanging fruit; IEEE transactions on EC – publish more things like case-studies
\n2) evolving classifiers research – emailed Jensen
\n3) something more substantial with tags – Lee has faith in inexact matching – can we find a way to fit this into probabilistic matching? well, matching is probabilistic anyway via the gp mechanism; maybe the real thing
\nsean luke – GECCO paper – benchmarks in GP<\/p>\n
emma stuff
\nget something for the evolutionary computation journal
\nmore theoretical people are in that journal
\nextend to other EC systems
\nextend beyond EC systems to other stochastic systems<\/p>\n
kyle stuff
\ndissertation – coevolution, lots of pages
\nkyles talk – autoconstruction
\ncan order be reduced to ILP? We know that ILP is intractable.
\nwhat about picking a problem that’s easier with probabilistic guarantees?
\nwhat about running TSP vs clique
\nhow do you measure “useful genetic material?”
\nrun cosmos on the problems – look at the behavior<\/p>\n","protected":false},"excerpt":{"rendered":"
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