A framework for analyzing the computational capabilities and the limitations of the evolutionary process of random change guided by selection was recently introduced by Valiant [Val06]. In his framework the process of acquiring a complex functionality is viewed as a constrained form of PAC learning. In addition to the basic definition, a number of natural variants of the evolvability model were introduced by Valiant, and several others have been suggested since then [Val09, Mic07, Val08]. Understanding the relative power of these variants in terms of the efficiently evolvable function classes they define is one of the main open problems regarding the model [Val09, FV08]. We present several results that collectively demonstrate that the notion of evolvability is robust to a variety of reasonable modifications of the model. Our results show that the power of a model of evolvability essentially depends only on the fitness metric used in the model. In particular, we prove that the cla...
Leslie G. Valiant