Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Scalable learning in genetic programming using automatic function definition
Advances in genetic programming
Rank aggregation methods for the Web
Proceedings of the 10th international conference on World Wide Web
Convergence of the Nelder--Mead Simplex Method to a Nonstationary Point
SIAM Journal on Optimization
Multimeme Algorithms for Protein Structure Prediction
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Advanced Population Diversity Measures in Genetic Programming
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Protein Structure Prediction by Applying an Evolutionary Algorithm
IPDPS '03 Proceedings of the 17th International Symposium on Parallel and Distributed Processing
Coordination number prediction using learning classifier systems: performance and interpretability
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Prediction of topological contacts in proteins using learning classifier systems
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Evolutionary and Metaheuristics based Data Mining (EMBDM); Guest Editors: José A. Gámez, María J. del Jesús, José M. Puerta
Protein Folding in Simplified Models With Estimation of Distribution Algorithms
IEEE Transactions on Evolutionary Computation
Evolutionary symbolic discovery for bioinformatics, systems and synthetic biology
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Genetic programming needs better benchmarks
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Better GP benchmarks: community survey results and proposals
Genetic Programming and Evolvable Machines
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One of the key elements in protein structure prediction is the ability to distinguish between good and bad candidate structures. This distinction is made by estimation of the structure energy. The energy function used in the best state-of-the-art automatic predictors competing in the most recent CASP (Critical Assessment of Techniques for Protein Structure Prediction) experiment is defined as a weighted sum of a set of energy terms designed by experts. We hypothesised that combining these terms more freely will improve the prediction quality. To test this hypothesis, we designed a genetic programming algorithm to evolve the protein energy function. We compared the predictive power of the best evolved function and a linear combination of energy terms featuring weights optimised by the Nelder---Mead algorithm. The GP based optimisation outperformed the optimised linear function. We have made the data used in our experiments publicly available in order to encourage others to further investigate this challenging problem by using GP and other methods, and to attempt to improve on the results presented here.