Research Output
Can HP-protein folding be solved with genetic algorithms? Maybe not
  Genetic algorithms might not be able to solve the HP-protein folding problem because creating random individuals for an initial population is very hard, if not impossible. The reason for this, is that the expected number of constraint violations increases with instance size when randomly sampling individuals, as we will show in an experiment. Thereby, the probability of randomly sampling a valid individual decreases exponentially with instance size. This immediately prohibits resampling, and repair mechanisms might also be non-applicable. Backtracking could generate a valid random individual, but it runs in exponential time, and is therefore also unsuitable. No wonder that previous approaches do not report how (often) random samples are created, and only address small instances. We contrast our findings with TSP, which is also NP-hard, but does not have these problems.

  • Date:

    30 November 2023

  • Publication Status:


  • Publisher


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  • Funders:

    Edinburgh Napier Funded


Jansen, R., Horn, R., van Eck, O., Version, K., Thomson, S. L., & van den Berg, D. (2023). Can HP-protein folding be solved with genetic algorithms? Maybe not. In Proceedings of the 15th International Joint Conference on Computational Intelligence (131-140).



Protein Folding, Genetic Algorithms, Evolutionary Computing, Constraints, Constraint Hierarchy

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