Evolutionary Fitness in Variable Environments
One essential ingredient of evolutionary theory is the concept of fitness as a measure for a species' success in its living conditions. Here, we quantify the effect of environmental fluctuations onto fitness by analytical calculations on a general evolutionary model and by studying corresponding individual-based microscopic models. We demonstrate that not only larger growth rates and viabilities, but also reduced sensitivity to environmental variability substantially increases the fitness. Even for neutral evolution, variability in the growth rates plays the crucial role of strongly reducing the expected fixation times. Thereby, environmental fluctuations constitute a mechanism to account for the effective population sizes inferred from genetic data that often are much smaller than the census population size.
Code (0)
등록된 구현이 없습니다.
Tasks
SensitivitySimilar Papers 제목 키워드 기반
Mimicking Evolution with Reinforcement Learning
Evolution gave rise to human and animal intelligence here on Earth. We argue that the path to developing artificial human-like-intelligence will pass through mimicking the evolutionary process in a nature-like simulation…
Evolutionary Algorithmsreinforcement-learningReinforcement LearningReinforcement Learning (RL)Fitness and Overfitness: Implicit Regularization in Evolutionary Dynamics
A common assumption in evolutionary thought is that adaptation drives an increase in biological complexity. However, the rules governing evolution of complexity appear more nuanced. Evolution is deeply connected to learn…
A Driven Disordered Systems Approach to Biological Evolution in Changing Environments
Biological evolution of a population is governed by the fitness landscape, which is a map from genotype to fitness. However, a fitness landscape depends on the organisms environment, and evolution in changing environment…
Fitness Approximation through Machine Learning
We present a novel approach to performing fitness approximation in genetic algorithms (GAs) using machine-learning (ML) models, through dynamic adaptation to the evolutionary state. Maintaining a dataset of sampled indiv…
Codynamic Fitness Landscapes of Coevolutionary Minimal Substrates
Coevolutionary minimal substrates are simple and abstract models that allow studying the relationships and codynamics between objective and subjective fitness. Using these models an approach is presented for defining and…