Component weighting functions for adaptive search with EDAs
Intell. Syst. Group, Univ. of the Basque Country, Donostia
DOI: 10.1109/CEC.2008.4631352 Conference: Proceedings of the IEEE Congress on Evolutionary Computation, CEC 2008, June 1-6, 2008, Hong Kong, China
This paper introduces the component weighting approach as a general optimization heuristic to increase the likelihood of escaping from local optima by dynamically modifying the fitness function. The approach is tested on the optimization of the simplified hydrophobic-polar (HP) protein problem using estimation of distribution algorithms (EDAs). We show that the use of component weighting together with statistical information extracted from the set of selected solutions considerably improve the results of EDAs for the HP problem. The paper also elaborates on the use of probabilistic modeling for the definition of dynamic fitness functions and on the use of combinations of models.
Available from: Benhui Chen
- "Although more complex models have been proposed, the HP model remains a focus of research in computational biology, chemical and statistical physics. In evolutionary computation, the model is still employed because of its simplicity and its usefulness as a test-bed for new evolutionary optimization approaches . "
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ABSTRACT: The protein structure prediction (PSP) problem is one of the most important problems in computational biology. This paper proposes a novel Estimation of Distribution Algorithms (EDAs) based method to solve the PSP problem on HP model. Firstly, a composite fitness function containing the information of folding structure core formation is introduced to replace the traditional fitness function of HP model. It can help to select more optimum individuals for probabilistic model of EDAs algorithm. And a set of guided operators are used to increase the diversity of population and the likelihood of escaping from local optima. Secondly, an improved backtracking repairing algorithm is proposed to repair invalid individuals sampled by the probabilistic model of EDAs for the long sequence protein instances. A detection procedure of feasibility is added to avoid entering invalid closed areas when selecting directions for the residues. Thus, it can significant reduce the number of backtracking operation and the computational cost for long sequence protein. Experimental results demonstrate that the proposed method outperform the basic EDAs method. At the same time, it is very competitive with the other existing algorithms for the PSP problem on lattice HP models.
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ABSTRACT: This paper discusses exact learning of Bayesian networks in estimation of distribution algorithms. The estimation of Bayesian
network algorithm (EBNA) is used to analyze the impact of learning the optimal (exact) structure in the search. By applying
recently introduced methods that allow learning optimal Bayesian networks, we investigate two important issues in EDAs. First,
we analyze the question of whether learning more accurate (exact) models of the dependencies implies a better performance
of EDAs. Secondly, we are able to study the way in which the problem structure is translated into the probabilistic model
when exact learning is accomplished. The results obtained reveal that the quality of the problem information captured by the
probability model can improve when the accuracy of the learning algorithm employed is increased. However, improvements in
model accuracy do not always imply a more efficient search.
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