In attendence : Lee, Kyle, Tom, Emma, Kwaku, Omri
three fundamental features of biological evolution (from abstract)
1) particulate genes carry some subtle consequences for biological evolution that have not yet translated mainstream EC
2) the adaptive properties of the genetic code illustrate how both communities can contribute to a common understanding of appropriate evolutionary abstractions
3) EC exploration of representational language and its role in the genotype/phenotype debate
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.
can approximation algorithms make seemingly inherently sequential problems into parallel problems?
“conscious intelligence vs natural selection”
i.e. engineering vs science
i.e. optimal solutions vs approximate solutions
randomness not just of the RV but of the model?
meta-randomness
second-order randomness
probabilistic programming languages as second order randomness?
need padding in randomness, redundancy to ensure robustness
buidling a system that is sound and consistent but not complete – but GPs are already turing complete
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
what is the purpose of having EC mimic biology?
(1) to make adaptive programs, following the intuition that evolution is a powerful force we need to come to understand
(2) to better understand biology and complex stochastic systems
this paper references Ostrowski and Reynolds as people who are studying EC from a search perspective
(1) PARTICULATE GENES AND POPULATION GENETICS
(2) THE ADAPTIVE GENETIC CODE
tom’s thoughts:
(3) THE DICHOTOMY OF GENOTYPE AND PHENOTYPE
genetic drift as a desirable characteristic
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
my angle: redundancy – it’s everywhere! doesn’t conflict with the bayesian stuff either
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.
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).”
“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.”
“co-adapted gene complexes” – Fisher, 1930 and O’Reilly 1999
Adaptive cookbook = REDUNDANCY
error minimizing code smoothing the fintess landscape? – comes from upublished data
lee’s stuff
tag space machine
works with stacks, all code lives in the tag space
ratio tags, denseness of numbers
cucumber – got the R spec and cucumber book
smaller GP steps into cycle
some red/green thing?
1) penumbra
2) keep cool? – climate change negotiation game between countries – can we find or quickly write a simulator?
3) Zeek Nieremberg – new lab member – filing Div 3, mostly a natural science guy
4) physical space, course partcipation – GP and intro class on creativity
tom stuff
1) 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
2) evolving classifiers research – emailed Jensen
3) 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
sean luke – GECCO paper – benchmarks in GP
emma stuff
get something for the evolutionary computation journal
more theoretical people are in that journal
extend to other EC systems
extend beyond EC systems to other stochastic systems
kyle stuff
dissertation – coevolution, lots of pages
kyles talk – autoconstruction
can order be reduced to ILP? We know that ILP is intractable.
what about picking a problem that’s easier with probabilistic guarantees?
what about running TSP vs clique
how do you measure “useful genetic material?”
run cosmos on the problems – look at the behavior
