Evolvability Is Inevitable: Increasing Evolvability Without the Pressure to Adapt
arXiv:1302.1143 · doi:10.1371/journal.pone.0062186
Abstract
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.
Cited by in corpus (12)
- Born to Learn: the Inspiration, Progress, and Future of Evolved Plastic Artificial Neural Networks
- Analysis of Evolutionary Diversity Optimisation for Permutation Problems
- Defending Active Directory by Combining Neural Network based Dynamic Program and Evolutionary Diversity Optimisation
- Evolving the Behavior of Machines: From Micro to Macroevolution
- A World of Views: A World of Interacting Post-human Intelligences
- Evolvability ES: Scalable and Direct Optimization of Evolvability
- Evolvability signatures of generative encodings: beyond standard performance benchmarks
- Simple Genetic Operators are Universal Approximators of Probability Distributions (and other Advantages of Expressive Encodings)
- On the feasibility of saltational evolution
- Population-Based Evolution Optimizes a Meta-Learning Objective
- A summary of the prevalence of Genetic Algorithms in Bioinformatics from 2015 onwards
- Discovering Evolutionary Stepping Stones through Behavior Domination