Evolvability Is Inevitable: Increasing Evolvability without the Pressure to Adapt

By Joel Lehman, Kenneth O. Stanley. 2013

70 citations recorded by the source corpus.

Why evolvability appears to have increased over evolutionary time is an important unresolved biological question. Unlike most candidate explanations, this paper proposes that increasing evolvability can result without any pressure to adapt. The insight is that if evolvability is heritable, then an unbiased drifting process across genotypes can still create a distribution of phenotypes biased towards evolvability, because evolvable organisms diffuse more quickly through the space of possible phenotypes. Furthermore, because phenotypic divergence often correlates with founding niches, niche founders may on average be more evolvable, which through population growth provides a genotypic bias towards evolvability. Interestingly, the combination of these two mechanisms can lead to increasing evolvability without any pressure to out-compete other organisms, as demonstrated through experiments with a series of simulated models. Thus rather than from pressure to adapt, evolvability may inevitably result from any drift through genotypic space combined with evolution's passive tendency to accumulate niches.

Subfields: Ecology, Genetics, Sociology and Political Science

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Citations in the corpus

Born to Learn: the Inspiration, Progress, and Future of Evolved Plastic Artificial Neural Networks (2017, background): “selection criteria, the concepts of evolvability and scalability (Wagner and Altenberg, 1996; Pigliucci, 2008; Lehman and Stanley, 2013), the encoding of genetic information through the genotype-phenotype mapping processes (Wagner and Altenberg, 1996; Hornby et al.”
Four concepts for resilience and the implications for the future of resilience engineering (2015, background): “Studies of biological systems [17] and evolutionary computational modeling of biological systems [23,24] have shown that properties that will sustain adaptive capacity in the future can be selected for [4].”
Quality Diversity: A New Frontier for Evolutionary Computation (2016, background): “For example, organisms that are sufficiently different from their predecessors may establish a new niche in which they enjoy greatly reduced competition and thus are more likely to survive (Kirschner and Gerhart, 1998; Lehman and Stanley, 2013).”
Minimal criterion coevolution: a new approach to open-ended search (2017, background): “QD algorithms to date rely on identifying and hence rewarding novelty to facilitate divergence, which leads to the need for BCs (descriptors), but divergence is also in principle a byproduct of simple drift [12] subject to a minimal criterion (MC) constraint for reproduction.”