By Osvaldo Gervasi, Beniamino Murgante, Sanjay Misra, Ana Maria A.C. Rocha, Carmelo M. Torre, David Taniar, Bernady O. Apduhan, Elena Stankova, Shangguang Wang
The five-volume set LNCS 9786-9790 constitutes the refereed lawsuits of the sixteenth foreign convention on Computational technological know-how and Its functions, ICCSA 2016, held in Beijing, China, in July 2016.
The 239 revised complete papers and 14 brief papers provided at 33 workshops have been conscientiously reviewed and chosen from 849 submissions. they're geared up in 5 thematical tracks: computational tools, algorithms and medical purposes; excessive functionality computing and networks; geometric modeling, photographs and visualization; complex and rising purposes; and data platforms and technologies.
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Extra info for Computational Science and Its Applications -- ICCSA 2016: 16th International Conference, Beijing, China, July 4-7, 2016, Proceedings, Part III
Ranging from the familiar TSP and QAP to general function optimization problems, GLS sits atop many well-known algorithms such as Genetic Algorithm (GA), Simulated Annealing (SA) and Tabu Search (TS). With lesser parameters to adjust to, GLS is relatively simple to implement and apply in many problems. This paper focuses on the potential applications of GLS in ligand docking problems via drug design. Over the years, computer aided drug design (CADD) has spearheaded the drug design process, whereby much focus has been trained on efﬁcient searching in de novo drug design.
37, 3994–4002 (1994). 1021/jm00049a019 21. : Evolutionary algorithms for de novo drug design – A survey. Appl. Soft Comput. 27, 543–552 (2015) 22. : Solving molecular ﬂexible docking problems with metaheuristics: a comparative study. Appl. Soft Comput. 28, 379– 393 (2015) 23. : Automated docking of highly ﬂexible ligands by genetic algorithms: a critical assessment. J. Comput. Chem. 25, 412–422 (2003) 24. : Molecular recognition of receptor sites using a genetic algorithm with a description of desolvation.
Each feature will be associated with a cost and penalty, which are the terms and coefﬁcients from the objective function. When the algorithm settles in local optima, the cost function is augmented by accumulating penalties on selected features. These penalty terms are dynamically manipulated throughout the course of the search to steer the heuristic towards more viable solutions. Naturally, the overall cost will be greatly affected by costly features. This way, GLS is able to focus and distribute its searching efforts into more promising areas besides avoiding the accumulation of unnecessary workforce in any one region of the search space.