Protein structure prediction with co-evolving memetic algorithms

We present a coevolutionary learning-optimisation approach to protein structure prediction which uses a memetic algorithm as its underlying search method. Instance-specific knowledge can be learned, stored and applied by the system in the form of a population of rules. These rules determine the neighbourhoods used by the local search process, which is applied to each member of the coevolving population of candidate solutions. A generic coevolutionary framework is proposed for this approach, and then the implementation of a simple self-adaptive instantiation is described. A rule defining the local search\´s move operator is encoded as a {condition : action} pair and added to the genotype of each individual. It is demonstrated that the action of mutation and crossover on the patterns encoded in these rules, coupled with the action of selection on the resultant phenotypes is sufficient to permit the discovery and propagation of knowledge about the instance being optimised. The algorithm is benchmarked against a simple genetic algorithm, a memetic algorithm using a fixed neighbourhood function, and a similar memetic algorithm which uses random (rather than evolved) rules and shows significant improvements in terms of the ability to locate optimum configurations using Dill\´s HP model. It is shown that this "metalearning" of problem features provides a means of creating highly scaleable algorithms.