scholarly journals Semi-deterministic and genetic algorithms for global optimization of microfluidic protein-folding devices

2006 ◽  
Vol 66 (2) ◽  
pp. 319-333 ◽  
Author(s):  
Benjamin Ivorra ◽  
David E. Hertzog ◽  
Bijan Mohammadi ◽  
Juan G. Santiago
Keyword(s):  
Genetic Algorithms ◽  
Protein Folding ◽  
Global Optimization

Implementation of Eurocode Load Cases in Optimization Problems of Steel Frames, Based on Genetic Algorithms

2013 ◽  
Vol 310 ◽  
pp. 609-613
Author(s):  
Ioana D. Balea ◽  
Radu Hulea ◽  
Georgios E. Stavroulakis
Keyword(s):  
Finite Element ◽  
Genetic Algorithms ◽  
Global Optimization ◽  
Optimal Design ◽  
Optimization Problems ◽  
Steel Frames ◽  
Global Optimization Algorithm ◽  
Planar Structures ◽  
Discrete Global Optimization ◽  
Steel Sections

This paper presents an implementation of Eurocode load cases for discrete global optimization algorithm for planar structures based on the principles of finite element methods and genetic algorithms. The final optimal design is obtained using IPE sections chosen as feasible by the algorithm, from the available steel sections from industry. The algorithm is tested on an asymmetric planar steel frame with promising results.

Incorporating Twinkling in Genetic Algorithms for Global Optimization

2008 ◽  
Author(s):  
George S. Ladkany ◽  
Mohamed B. Trabia
Keyword(s):  
Genetic Algorithms ◽  
Global Optimization ◽  
Rate Of Convergence ◽  
Weighted Average ◽  
Distributed Arithmetic ◽  
Design Problems ◽  
Random Deviation ◽  
Engineering Design Problems ◽  
Crossover Phase ◽  
Simple Genetic Algorithms

Genetic algorithms have been extensively used as a reliable tool for global optimization. However these algorithms suffer from their slow convergence. To address this limitation, this paper proposes a two-fold approach to address these limitations. The first approach is to introduce a twinkling process within the crossover phase of a genetic algorithm. Twinkling can be incorporated within any standard algorithm by introducing a controlled random deviation from its standard progression to avoiding being trapped at a local minimum. The second approach is to introduce a crossover technique: the weighted average normally-distributed arithmetic crossover that is shown to enhance the rate of convergence. Two possible twinkling genetic algorithms are proposed. The performance of the proposed algorithms is successfully compared to simple genetic algorithms using various standard mathematical and engineering design problems. The twinkling genetic algorithms show their ability to consistently reach known global minima, rather than nearby sub-optimal points with a competitive rate of convergence.

Evolutionary Computing to Examine Variation in Proteins with Evolution

2016 ◽  
pp. 187-202
Author(s):  
Sujay Ray
Keyword(s):  
Genetic Algorithms ◽  
Global Optimization ◽  
Amino Acid ◽  
Residue Level ◽  
Amino Acid Sequences ◽  
Single Amino Acid ◽  
Protein Alignment ◽  
Computational Approaches ◽  
Structure Of Proteins ◽  
State Of Art

Amino-acid sequences play a pivotal role for the structure of proteins. Alterations in a single amino-acid may vary the protein functioning. Alignment of sequences recognizes evolutionary and structurally related residues in a group of amino-acid sequences. It also aids to perceive the regions that are conserved throughout and are also functionally important. Although protein alignment issue has been studied in the past decades, but computational approaches serves as more accurate to investigate the entire process in a comparably lesser time. Evolutionary algorithms, more specifically, genetic algorithms are very beneficial. It leads to the global optimization of the protein after observance of “the fittest” among the rest. On global optimization, the protein tends to be more stable, thereby, helping the process of interactions among other stable proteins and provides a residue level study. Thus, this state-of-art can be implemented for alignment of macro-molecules, which serves as an essential criterion for further molecular level analyses.

Global Optimization in Protein Folding

2008 ◽  
pp. 1392-1411
Author(s):  
Daniel R. Ripoll ◽  
Adam Liwo ◽  
Harold A. Scheraga
Keyword(s):  
Protein Folding ◽  
Global Optimization
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