Models of Genetic Algorithms

Buratti, S., Ballabio, D., BenedetK, S., and Cosio, M. S. (2007). PredicKon of Italian red wine sensorial descriptors from electronic nose, electronic tongue and spectrophotometric measurements by means of Genetic Algorithm regression models. Food Ghent. 100(1), 211-218. 

Two aspects of the system must be modeled in order to use the idea of genetic algorithms for an application like molecular docking. First, a linear representation of a configuration such as a bit string is needed describing all... 

Variable selection is performed by using Genetic Algorithms (GA), based on the evolution of a population of models. In genetic algorithm terminology, the binary vector I is called a chromosome, which is a p-dimensional vector where each position (a gene) corresponds to a variable (1 if included in the model, 0 otherwise). Each chromosome represents a model with a subset of variables. 

The developed optimization is solved with genetic algorithm as the previous study based on deterministic optimization techniques showed that it is often trapped in local optima, due to highly non-linear nature of formulations in the model. The simulation model and genetic algorithm is interacted to produce high quality optimal solution(s), although computational time is relatively expensive. 

Shum, Y.S and Gong, D.C. (2006). The Application of Genetic Algorithm in the Development of Preventive Maintenance Analytic Model. The International Journal of Advanced Manufacturing Technology. Vol. 32, pp. 169-183. 

Ando S. and Iba H. (2001). Inference of gene regulatory model by genetic algorithms. Proceedings of IEEE Congress on Evolutionary Confutation, pp 712-719. 

Hou, T.-J. and Xu, X. (2002) ADME evaluation in drug discovery. 1. Applications of genetic algorithms on the prediction of blood-brain partitioning of a large set drugs./. Mol, Model, 8, 337-349. 

T. Brodmeier and E. Pretsch,. Comput. Chem., IS, 588 (1994). Application of Genetic Algorithms in Molecular Modeling. 

The MOD model uses Genetic Algorithms techniques (Freni E. 1997), (Holland J. 1992), (Holland J. 1997) as an optimization technique in function of its high applicability in complex problems (DUNCAN R. 1990), (FALKENAUERE 1999). 

In the future, the model will be refined and enhanced to integrate limited spare parts and maintenance persoimel. The analysis will be enhanced to evaluate more detailed characteristics of the system, for example the productivity of the system. Furthermore, the application of genetic algorithms for an automated optimization of system parameters shall be investigated. 

---