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The emerging view in molecular biology is that molecules are intrinsically dynamic systems rearranging themselves into different structures to interact with molecules in the cell. Such rearrangements take place on energy landscapes that are vast and multimodal, with minima housing alternative structures. The multiplicity of biologically-active structures is prompting researchers to expand their treatment of classic computational biology problems, such as the template-free protein structure prediction problem (PSP), beyond the quest for the global optimum. In this paper, we revisit subpopulation-oriented EAs as vehicles to switch the objective from classic optimization to landscape mapping. Specifically, we present two EAs, one of which makes use of subpopulation competition to allocate more computational resources to fitter subpopulations, and another of which additionally utilizes a niche preservation technique to maintain stable and diverse subpopulations. Initial assessment on benchmark optimization problems confirms that stabler subpopulations are achieved by the niche-preserving EA. Evaluation on unknown energy landscapes in the context of PSP demonstrates superior mapping performance by both algorithms over a popular Monte Carlo-based method, with the niche-preserving EA achieving superior exploration of lower-energy regions. These results suggest that subpopulation EAs hold much promise for solving important mapping problems in computational structural biology.

Using subpopulation EAs to map molecular structure landscapes