Imprecise selection and fitness approximation in a large-scale evolutionary rule based system for blood pressure prediction

Genetic and Evolutionary Computation Conference (GECCO) 2015

Publication date: July 15, 2015

Erik Hemberg, Kalyan Veeramachaneni, Franck Dernoncourt, Mark Wagy, Una-May O'Reilly

We present how we have strategically allocated fitness evaluations in a large-scale rule based evolutionary system called ECStar. We describe a strategy that culls potentially weaker solutions early, then later only compete with solutions which have equivalent fitness evaluations, as they are evaluated on more fitness cases. Despite incurring some imprecision in fitness comparison, which arises from not evaluating on all the fitness cases or even the same ones, the strategy allows our system to make effective progress when the resources at its disposal are unpredictably available.

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Research Area:  Adobe Research iconAI & Machine Learning