Computational Questions in Evolution

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Computational Questions in Evolution

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Title: Computational Questions in Evolution
Author: Kanade, Varun
Citation: Kanade, Varun. 2012. Computational Questions in Evolution. Doctoral dissertation, Harvard University.
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Abstract: Darwin's theory (1859) proposes that evolution progresses by the survival of those individuals in the population that have greater fitness. Modern understanding of Darwinian evolution is that variation in phenotype, or functional behavior, is caused by variation in genotype, or the DNA sequence. However, a quantitative understanding of what functional behaviors may emerge through Darwinian mechanisms, within reasonable computational and information-theoretic resources, has not been established. Valiant (2006) proposed a computational model to address the question of the complexity of functions that may be evolved through Darwinian mechanisms. In Valiant's model, the goal is to evolve a representation that computes a function that is close to some ideal function under the target distribution. While this evolution model can be simulated in the statistical query learning framework of Kearns (1993), Feldman has shown that under some constraints the reverse also holds, in the sense that learning algorithms in this framework may be cast as evolutionary mechanisms in Valiant's model. In this thesis, we present three results in Valiant's computational model of evolution. The first shows that evolutionary mechanisms in this model can be made robust to gradual drift in the ideal function, and that such drift resistance is universal, in the sense that, if some concept class is evolvable when the ideal function is stationary, it is also evolvable in the setting when the ideal function drifts at some low rate. The second result shows that under certain de nitions of recombination and for certain selection mechanisms, evolution with recombination may be substantially faster. We show that in many cases polylogarithmic, rather than polynomial, generations are sufficient to evolve a concept class, whenever a suitable parallel learning algorithm exists. The third result shows that computation, and not just information, is a limiting resource for evolution. We show that when computational resources in Valiant's model are allowed to be unbounded, while requiring that the information-theoretic resources be polynomially bounded, more concept classes are evolvable. This result is based on widely believed conjectures from complexity theory.
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