Genetic Algorithm Techniques

Istadi and Amin, N. A. S. (2006). Hybrid Artificial Neural Network-Genetic Algorithm Technique for Modeling and Optimization of Plasma Reactor, Ind. Eng. Chem. Res., 45, pp. 6655-6664. 

We have developed a MATLAB code that uses Genetic Algorithm technique to estimate the parameters of the kinetic model based on the carbide mechanism, and demonstrated that this code works using data from the literature [14]. A high pressure bench-scale reactor unit has been commissioned to conduct FT experiments in the gas phase and supercritical hydrocarbon solvent phase. Here, we have reported the preliminary results from a set of gas phase FT experiments conducted using our rig. 

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). 

Other methods which are applied to conformational analysis and to generating multiple conformations and which can be regarded as random or stochastic techniques, since they explore the conformational space in a non-deterministic fashion, arc genetic algorithms (GA) [137, 1381 simulation methods, such as molecular dynamics (MD) and Monte Carlo (MC) simulations 1139], as well as simulated annealing [140], All of those approaches and their application to generate ensembles of conformations arc discussed in Chapter II, Section 7.2 in the Handbook. 

Simulated Annealing-based solutions [19] are conceptually the same as Genetic Algorithm-based approaches. However, the SA-based techniques, in our experience, are more sensitive to the initial settings of the parameters. Nevertheless, once the correct ones are found, the method can achieve the efficiency of GA-based solutions. We must point out that SA-based solutions have never outperformed the GA-based ones in our studies. Much of what has been mentioned regarding the GA-based solutions is also relevant for the SA technique, particularly, with respect to the cost functions. 

A molecular dynamics simulation samples the phase space of a molecule (defined by the position of the atoms and their velocities) by integrating Newton s equations of motion. Because MD accounts for thermal motion, the molecules simulated may possess enough thermal energy to overcome potential barriers, which makes the technique suitable in principle for conformational analysis of especially large molecules. In the case of small molecules, other techniques such as systematic, random. Genetic Algorithm-based, or Monte Carlo searches may be better suited for effectively sampling conformational space. 

The descriptor set can then be reduced by eliminating candidates that show such bad characteristics. Optimization techniques such as genetic algorithms (see Section 9.7) are powerful means of automating this selection process. 

Concomitantly with the increase in hardware capabilities, better software techniques will have to be developed. It will pay us to continue to learn how nature tackles problems. Artificial neural networks are a far cry away from the capabilities of the human brain. There is a lot of room left from the information processing of the human brain in order to develop more powerful artificial neural networks. Nature has developed over millions of years efficient optimization methods for adapting to changes in the environment. The development of evolutionary and genetic algorithms will continue. 

As might be expected, established optimisation techniques such as simulated annealing and genetic algorithms have been used to tackle the subset selection problem. These methods... 

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