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View Code? Open in Web Editor NEWCode for "Acclivation of Virtual Fitness Landscapes" (ALIFE, 2019) by Kovitz, Bender, and Poffald
Code for "Acclivation of Virtual Fitness Landscapes" (ALIFE, 2019) by Kovitz, Bender, and Poffald
A Brief History -or- How All These Files Got In This Repository --------------------------------------------------------------- In 2017, Ben Kovitz wrote some spreading-activation code in Clojure and tried running an evolutionary simulation of knobs+graph organisms (where knobs provide initial values to propagate by spreading activation through the graph to determine a phenotype at some designated nodes in the graph). It produced some visually spectacular acclivated virtual fitness functions with "buttes", but it wasn't clear what the result was. In 2018, Ben Kovitz and Marcela Poffald ran a bunch of experiments with varying parameters on the spreading-activation algorithm, and some "hill-climber" algorithms to try to measure how much acclivation occurs and under what conditions. Experiments took all night, and acclivation seemed to occur unpredictably. We still had to look at plots of the virtual fitness functions to see if acclivation had occurred. Under some parameters, it occurred seldom; tiny changes to the parameters would make it occur rarely, and it wasn't at all clear why. Dave Bender and Ben Kovitz rewrote the whole thing in C with some Python support to make it easier to see plots of the various functions. The simulations ran in seconds rather than hours, but it still wasn't clear what makes acclivation likely vs. unlikely. We added more parameters and tried more experiments but we did not get predictable results. Even under more-favorable parameter settings, acclivation did not as often as we though it should, given the simple idea for why it should work--and our old observations of runs with now-forgotten parameter settings where acclivation happened on almost every run. In 2019, we tried again, inspired by a suggestion from Etienne Barnard to try feed-forward (as opposed to spreading-activation) networks and to allow larger populations. The feed-forward networks performed even worse, but the new experiments led us to figure out that only a narrow range of the S constant in the transfer function (see the ALIFE paper) works, and why: only then do cyclic compositions of the transfer function create a highly tunable set of ranges of expansion and contraction of the global fitness function. The Clojure code in 2017 happened to have the S constant in this range. It had been chosen for an unrelated spreading-activation experiment in order to put attractors at -0.5 and +0.5.
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