Genetic algorithms parallel

Fraga, E. S. and T. R. S. Matias. Synthesis and Optimization of a Nonideal Distillation System Using a Parallel Genetic Algorithm. Comput Chem Eng 20 (Suppl) S79-S84 (1996). 

The yield of the expected reaction product was used in an example as the feedback to a genetic algorithm (GA) driven method that proposes a new set of reaction conditions. After some cycles of synthesis and analysis the yield of this reaction was significantly improved by using better reaction conditions. In a second step, a set of different MCRs using a set of different conditions for each of them was carried out in parallel and optimized with a GA to find common optimal conditions [29]. 

Levine, D. (1996). "User s guide to the PGAPack parallel genetic algorithm library." Rep. No. 

Application of Parallel Computing Concepts in Genetic Algorithms 87... 

DE Differential evolution EDP Evolutionary distribution plot EPP Evolutionary progress plot ETP Evolutionary trajectory plot GA Genetic algorithm PGA Parallel genetic algorithm... 

Cantu-Paz E (1998) A survey of parallel genetic algorithms. In Calculateurs paralleles, reseaux et systems repartis, vol 10. Hermes, Paris, pp 141-171... 

Vivardli, F Giusti, G., Villani, M Campanini, R., Fariselli, P., Compiani, M. Casadio, R. (1995). LG ANN a parallel system combining a local genetic algorithm and neural networks for the prediction of secondary structure of proteins. ComputAppl Biosci 11,253-60. 

Vivarelli et al. (1995) used a hybrid system that combined a local genetic algorithm (LGA) and neural networks for the protein secondary structure prediction. The LGA, a version of the genetic algorithms (GAs), was particularly suitable for parallel computational architectures. Although the LGA was effective in selecting different... 

Moscovitch, D., et al.. Determination of a unique solution to parallel proton transfer reactions using the genetic algorithm, Biophys. ]., 2004, 87, 47-57. 

Miihlenbein, H. How Genetic Algorithms Really Work. I. Mutation And Hill Climbing. In Parallel Problem Solving From Nature 2 Elsevier Science Publisher B. V., 1992. p 15-25. 

Bak, T. The interaction of mutation rate, selection, and self-adaptation within a genetic algorithm In Parallel Problem Solving from Nature 2 Manner, R., Manderick, B., Eds. Elsevier Science Publishers B. V. ... 

H. Muhlenbein, in Foundations of Genetic Algorithms, G. J. E. Rawlins, Ed., Morgan Kaufman, San Mateo, CA, 1991, 316 pp. Evolution in Time and Space—The Parallel Genetic Algorithm. 

H. Muhlenbein, M. Schomisch, and J. Born, Parall. Computing, 17, 619 (1991). A Parallel Genetic Algorithm as Function Optimizer. 

A. Ishikawa, T. Toya, Y. Totoki, and A. Konagaya, Institute for New Generation Computer Technology, Tokyo, Report No. ICOT TR-0849, 1993. Parallel Iterative Aligner with Genetic Algorithm. 

Weber et al. also applied a genetic algorithm to a modular reaction scheme, but optimized directly against an experimental enzyme assay as the fitness function, rather than a theoretical QSAR model. In 20 generations of parallel synthesis of 20 Ugi reactions, thrombin inhibition was increased from 1000 fiM to 0.22 iiM. 

Roeva, O. Real-World Application of Genetic Algorithms. In Tech, Rijeka (2012) Syam, W.P., Al-Harkan, I.M. Comparison of three meta heuristics to optimize hybrid flow shop scheduling problem with parallel machines. In WASET, vol. 62, pp. 271-278 (2010)... 

F owler, J., Homg, S. and Cochran, J., 2003. A hybridized genetic algorithm to solve parallel machine scheduling problems with sequence dependent setups. International Journal of Industrial Engineering-Theory Applications and Practice, 10(3), 232-243. 

Key words genetic algorithms, firzzy set theory, parallel machine scheduling, fabric cutting, apparel. 

Cheng, R., Gen, M. and Tozawa, T., 1995. Minmax earliness/tardiness scheduling in identical parallel machine system using genetic algorithms. Computers Industrial Engineering, 29, 513-517. 

E. Alba, J. M. Troya, Genetic Algorithms for Protocol Validation , Proceedings of the 4th conference on Parallel Problem Solving (PPSN IV), Berlin, Germany, September 1996. 

Deb, K., Agrawal, S., Pratab, A., Me-yarivan, T. 2000, A Fast Elitist Non-Dominated Sorting Genetic Algorithm for Multi-Objective Optimization NSGA-II. Parallel Problem Solving from Nature VI Conference, Paris, France, 2000, pp. 849-858. 

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