%0 Book Section %1 statphys23_0828 %A Cai, S.J. %A Jin, W.J. %A Chou, C.I. %B Abstract Book of the XXIII IUPAP International Conference on Statistical Physics %C Genova, Italy %D 2007 %E Pietronero, Luciano %E Loreto, Vittorio %E Zapperi, Stefano %K algorithm evolution folding greedy knowledge-based optimization problem protein statphys23 topic-10 %T Appling Knowledge-based Evolution Algorithm with Greedy Local Search method to Protein Folding Problem %U http://st23.statphys23.org/webservices/abstract/preview_pop.php?ID_PAPER=828 %X The Knowledge-based Evolution Algorithm (KEA) is a newly optimization algorithm. It tries to simulate the process of the knowledge development. The whole search procedure is composed of several generations of local search. In each generation of sampling, we retrieve certain useful knowledge and construct a knowledge database (called guiding functions). These functions are then used to guide the search in the next generation in addition to the consideration of minimum energy. In this kind of process, the guiding functions (or the knowledge) will evolve from one generation to another. The evolutionary guiding function will help to direct searching processes and to reach the global minimum faster. The KEA has been applied to various problems, such as X-ray crystallography, atomic cluster problem, political districting problem, traveling salesman problem and protein folding problem, etc... In our previous work of protein folding problem, we use the simulated annealing (SA) method and chain growth method for local search, and find a new best optimal result. However, a question about the importance of local search method is: Does the KEA still work with a lower efficient local search method? To answer this question, we choose the greedy algorithm as the local search method. Our results show that the KEA can improve the searching efficiency of greedy algorithm. In this presentation, we will illustrate how to apply KEA with greedy local search method to protein folding problem, and make a comparison with other algorithms.

@incollection{statphys23_0828, abstract = {The Knowledge-based Evolution Algorithm (KEA) is a newly optimization algorithm. It tries to simulate the process of the knowledge development. The whole search procedure is composed of several generations of local search. In each generation of sampling, we retrieve certain useful knowledge and construct a knowledge database (called guiding functions). These functions are then used to guide the search in the next generation in addition to the consideration of minimum energy. In this kind of process, the guiding functions (or the knowledge) will evolve from one generation to another. The evolutionary guiding function will help to direct searching processes and to reach the global minimum faster. The KEA has been applied to various problems, such as X-ray crystallography, atomic cluster problem, political districting problem, traveling salesman problem and protein folding problem, etc... In our previous work of protein folding problem, we use the simulated annealing (SA) method and chain growth method for local search, and find a new best optimal result. However, a question about the importance of local search method is: Does the KEA still work with a lower efficient local search method? To answer this question, we choose the greedy algorithm as the local search method. Our results show that the KEA can improve the searching efficiency of greedy algorithm. In this presentation, we will illustrate how to apply KEA with greedy local search method to protein folding problem, and make a comparison with other algorithms.}, added-at = {2007-06-20T10:16:09.000+0200}, address = {Genova, Italy}, author = {Cai, S.J. and Jin, W.J. and Chou, C.I.}, biburl = {https://www.bibsonomy.org/bibtex/2fb07f738e857813e4cb39ef4fb7408ce/statphys23}, booktitle = {Abstract Book of the XXIII IUPAP International Conference on Statistical Physics}, editor = {Pietronero, Luciano and Loreto, Vittorio and Zapperi, Stefano}, interhash = {06e48f8032f983338132c0311b05e503}, intrahash = {fb07f738e857813e4cb39ef4fb7408ce}, keywords = {algorithm evolution folding greedy knowledge-based optimization problem protein statphys23 topic-10}, month = {9-13 July}, timestamp = {2007-06-20T10:16:30.000+0200}, title = {Appling Knowledge-based Evolution Algorithm with Greedy Local Search method to Protein Folding Problem}, url = {http://st23.statphys23.org/webservices/abstract/preview_pop.php?ID_PAPER=828}, year = 2007 }