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Evolving generalists via dynamic sculpting of rugged landscapes

Evolving systems, be it an antibody repertoire in the face of mutating pathogens or a microbial population exposed to varied antibiotics, constantly search for adaptive solutions in time-varying fitness landscapes. Generalists correspond to genotypes that remain fit across diverse selective pressures; cross-reactive antibodies are much wanted but rare, while multi-drug resistant microbes are undesired yet prevalent. However, little is known about under what conditions such solutions with a high capacity to adapt would be efficiently discovered by evolution, as environmental changes alter the relative fitness and accessibility of neighboring genotypes. In addition, can epistasis --- the source of landscape ruggedness and path constraints --- play a different role, if the environments are correlated in time? We present a generative model to estimate the propensity of evolving generalists in rugged landscapes that are tunably related and cycling relatively slowly. We find that environment cycling can substantially facilitate the search for fit generalists by dynamically enlarging their effective basins of attraction. Importantly, these high performers are most likely to emerge at an intermediate level of both ruggedness and environmental relatedness, trading diversity for fitness and accessibility. Our work provides a conceptual framework to study evolution in correlated varying complex environments, and offers statistical understanding that suggests general strategies for speeding up the generation of broadly neutralizing antibodies or preventing microbes from evolving multi-drug resistance.