| Autor: |
Antonio Bolufé-Röhler, Alex Coto-Santiesteban, Marta Rosa Soto, Stephen Chen |
| Jazyk: |
English<br />Spanish; Castilian |
| Rok vydání: |
2014 |
| Předmět: |
|
| Zdroj: |
GECONTEC: Revista Internacional de Gestión del Conocimiento y la Tecnología, Vol 2, Iss 3, Pp 1-16 (2014) |
| Druh dokumentu: |
article |
| ISSN: |
2255-5684 |
| Popis: |
Computer modeling of protein-ligand interactions is one of the most important phases in a drug design process. Part of the process involves the optimization of highly multi-modal objective (scoring) functions. This research presents the Minimum Population Search heuristic as an alternative for solving these global unconstrained optimization problems. To determine the effectiveness of Minimum Population Search, a comparison with seven state-of-the-art search heuristics is performed. Being specifically designed for the optimization of large scale multi-modal problems, Minimum Population Search achieves excellent results on all of the tested complexes, especially when the amount of available function evaluations is strongly reduced. A first step is also made toward the design of hybrid algorithms based on the exploratory power of Minimum Population Search. Computational results show that hybridization leads to a further improvement in performance. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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