The question of whether learning has an effect on the evolutionary process sparked plenty of research in the fields of Biology and Machine Learning. After more than 100 years of discussion, the consensus is that learning influences the speed of evolutionary convergence to a specific genetic configuration.
Our work looks at the same question in dynamic environments where the optimal behavior changes cyclically between different configurations, thus agents never stop adapting.
We find that in this situation evolution alone favors agents that specialize to a specific configuration, while the combination of evolution and learning prevents specialized strategies to evolve. This result demonstrates that learning does not only influence the speed but also the outcome of evolution.
This work is relevant for the fields of Biology and Machine Learning, as it demonstrates a new effect that we hope will start a new thread of research.
Furthermore, our results might extend to other cyclically-changing contexts in other fields, for example opinion formation and polarization.
Keywords: Baldwin effect, Foraging, Specialist, Generalist, Environmental variability, Evolution, Agent-based modeling, Learning, Neural network, Reinforcement learning