Tuning environmental timescales to evolve and maintain generalists
Natural environments can present diverse challenges, but some genotypes
remain fit across many environments. Such generalists' can be hard to evolve,
out-competed by specialists fitter in any particular environment. Here,
inspired by the search for broadly-neutralising antibodies during B-cell
affinity maturation, we demonstrate that environmental changes on an
intermediate timescale can reliably evolve generalists, even when faster or
slower environmental changes are unable to do so. We find that changing
environments on timescales comparable to evolutionary transients in a
population enhances the rate of evolving generalists from specialists, without
enhancing the reverse process. The yield of generalists is further increased in
more complex dynamic environments, such as a chirp' of increasing frequency.
Our work offers design principles for how non-equilibrium fitness `seascapes'
can dynamically funnel populations to genotypes unobtainable in static
environments.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
The Evaluation Cost of Task Specialization in Evolutionary Multi-Robot Systems
Task specialization can improve the efficiency of multi-robot systems (MRSs). Previous works have investigated the emergence of task-specialist robot controllers through evolutionary optimization and have argued that tas…
On the Cost of Evolving Task Specialization in Multi-Robot Systems
Task specialization can lead to simpler robot behaviors and higher efficiency in multi-robot systems. Previous works have shown the emergence of task specialization during evolutionary optimization, focusing on feasibili…
Multi-Timescale Modeling of Human Behavior
In recent years, the role of artificially intelligent (AI) agents has evolved from being basic tools to socially intelligent agents working alongside humans towards common goals. In such scenarios, the ability to predict…
AI AgentMinecraftvalidEvolving generalist controllers to handle a wide range of morphological variations
Neuro-evolutionary methods have proven effective in addressing a wide range of tasks. However, the study of the robustness and generalizability of evolved artificial neural networks (ANNs) has remained limited. This has …
Toward a Diffusion-Based Generalist for Dense Vision Tasks
Building generalized models that can solve many computer vision tasks simultaneously is an intriguing direction. Recent works have shown image itself can be used as a natural interface for general-purpose visual percepti…
Conditional Image GenerationImage GenerationQuantization