Computational Questions in Evolution
Contributors: Valiant, Leslie G.; Kalai, Adam T.; Mitzenmacher, Michael D.; Vadhan, Salil P.
Published: 2012
Language: eng
Description: 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.
Notes: Keywords: computational learning, evolution.

Thesis Ph.D. Harvard University 2012