Evolvability

By Leslie G. Valiant. JACM, 2006

644 citations recorded by the source corpus.

Living organisms function in accordance with complex mechanisms that operate in different ways depending on conditions. Darwin's theory of evolution suggests that such mechanisms evolved through variation guided by natural selection. However, there has existed no theory that would explain quantitatively which mechanisms can so evolve in realistic population sizes within realistic time periods, and which are too complex. In this article, we suggest such a theory. We treat Darwinian evolution as a form of computational learning from examples in which the course of learning is influenced only by the aggregate fitness of the hypotheses on the examples, and not otherwise by specific examples. We formulate a notion of evolvability that distinguishes function classes that are evolvable with polynomially bounded resources from those that are not. We show that in a single stage of evolution monotone Boolean conjunctions and disjunctions are evolvable over the uniform distribution, while Boolean parity functions are not. We suggest that the mechanism that underlies biological evolution overall is “evolvable target pursuit”, which consists of a series of evolutionary stages, each one inexorably pursuing an evolvable target in the technical sense suggested above, each such target being rendered evolvable by the serendipitous combination of the environment and the outcomes of previous evolutionary stages.

Subfields: Artificial Intelligence, Genetics

View Paper: https://www.semanticscholar.org/paper/c7246381ba2bad891848c124e869c6e1a82f2d03

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Citations in the corpus

Evolvability as a function of purifying selection in TEM-1 β-lactamase. (2015, positive): “For example, recent studies (Draghi et al., 2010; Hayden et al., 2011; Payne and Wagner, 2014; Wagner, 2008) show that robustness can facilitate the adaptation of biological systems to environmental change—a property sometimes called ''evolvability'' (Kirschner and Gerhart, 1998).”
Modular Brain Networks (2015, background): “An important idea in evolutionary theory is that modularity in the organization of biological systems confers significant advantages in an evolutionary setting, by supporting adaptability and robustness and thus increasing the system's evolvability (Kirschner & Gerhart 1998).”
Minimal criterion coevolution: a new approach to open-ended search (2017, background): “In nature, individuals are segregated into species, each of which fills a particular ecological niche [6].”
Robustness and evolvability: a paradox resolved (2008, methodology): “Multiple definitions of robustness and evolvability are in use (Houle 1992; Kirschner & Gerhart 1998; Bedau & Packard 2003; Hansen 2003; Masel & Bergman 2003; Michod et al. 2003; Nijhout et al. 2003; Earl & Deem 2004; Aharoni et al. 2005; Wagner 2005; Sniegowski & Murphy 2006).”
Evolvability Search: Directly Selecting for Evolvability in order to Study and Produce It (2016, background): “2908838 extinction or to establish new niches [3, 13].”
Statistical Query Algorithms for Mean Vector Estimation and Stochastic Convex Optimization (2015, positive): “Additional results can be easily derived in a variety of other contexts that rely on statistical queries, such as evolvability (Valiant [92]), adaptive data analysis (Dwork et al. [32]), and distributed data analysis (Chu et al. [20]).”
Mutation as a Stress Response and the Regulation of Evolvability (2007, background): “The concept of evolvability relates to the intrinsic capacity of organisms to evolve, and reflects the extent of genetic variability in populations (Sniegowski and Murphy, 2006).”