Genetic Algorithm-Based Methods

GAs are a subset of evolutionary algorithms in which a population of abstract representations (called chromosomes) of candidate solutions (called individuals) to an optimisation problem evolves towards better solutions. The basic steps of the process are outlined in Fig. 7.6. Solutions are generally represented as binary vectors of Os and Is. At each step of an iterative process, the behaviour of each individual solution is evaluated using a fitness function, and the search process stops when the specified fitness criterion is reached. 

In the context of mechanism reduction, a 1 or 0 represents a particular species or reaction (Hem dez et al. 2010) being present or not within the final reduced 

A slightly different approach was applied in the earlier work of Edwards et al. (1998) where the search was for the minimum number of reactions/species needed to satisfy specified error bounds rather than for the best reduced mechanism for a fixed number of species and a given error tolerance. A heuristic comparison was made in this work between the computational expense of the GA approach and global sensitivity-based methods. The number of functional evaluations for the GA approach was stated to be lower than for global sensitivity analysis, although the same would not be true for the local rate sensitivity and DRG-based methods described above. The potential user therefore has the choice between applying global methods such as optimisation with the associated computational expense 

Sikalo et al. (2014) compared several options for the application of genetic algorithms to mechanism reduction, exploring the trade-off between the size and accuracy of the resulting mechanisms. Information on the speed of solution was also taken into account, so that, for example, the least stiff system (Sect. 6.7) could be selected. An automatic method for the reduction of chemical kinetic mechanisms was suggested and tested for the performance of reduced mechanisms used within homogeneous constant pressure reactor and burner-stabilised flame simulations. The flexibility of this type of approach has clear utility when restrictions are placed on the number of variables that can be tolerated within a scheme in the computational sense. However, the development of skeletal mechanisms is rarely the end point of any reductiOTi procedure since the application of lumping or timescale-based methods can be applied subsequently. These methods will be discussed in later sections. 

---

Judson R S, E P Jaeger and A M Treasurywala 1994. A Genetic Algorithm-Based Method for Dockin Flexible Molecules. Journal of Molecular Structure Theochem 114 191-206. 

RS Judson, EP Jaeger, AM Treasurywala. A genetic algorithm based method for docking fiexible molecules. THEOCHEM 114 191-206, 1994. 

R. S. Judson, E. P. Jaeger, and A. M. Treasurywala, J. Mol. Struct. (THEOCHEM), 308,191 (1994). A Genetic Algorithm-Based Method for Docking Flexible Molecules. 

Pearlman DA (1996) Fingar a new genetic algorithm based method for fitting NMR data. J Biomol NMR8(l) 49-66... 

Bangalore, A. S., Shaffer, R. E., Small, G. W. and Arnold, M. (1996) Genetic algorithm-based method for selecting wavelengths and model size for use with partial least squares regression. Application to near infrared spectroscopy. Analytical Chemistry, 68, 4200-12. 

Sikalo, N., Hasemann, O., Schulz, C., Kempf, A., Wlokas, I. A genetic algorithm-based method for the automatic reduction of reaction mechanisms. Int J Chem. Kinet. 46, 41-59 (2014) Singer, M.A., Pope, S.B. Exploiting ISAT to solve the reaction-diffusion equation. Combust. Theory Model 8, 361-383 (2004)... 

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. 

Wegner, J.K. and Zell, A. Prediction of aqueous solubility and partition coefEcient optimized by a genetic algorithm based descriptor selection method. /. Chem. Inf. Comput. Sci. 2003, 43, 1077-1084. 

Other methods such as Genetic Algorithm based on evolution principles, maximum entropy method based on Bayesian theory, and maximum likelihood methods have also been developed. ... 

Fig. 1. Statistical classification strategy (SCS) a schematic road map of how the SCS method is developed for individual databases. GA ORS, genetic algorithm based optimal region selection LDA, linear discriminant analysis LOO, leave-one-out (method of cross-validation) coeff, coefficients.

Solubility and Partition Coefficient Optimized by a Genetic Algorithm Based Descriptor Selection Method. 

Wegner JK, Zell A (2003) Prediction of aqueous solubility and partition coefficient optimized by genetic algorithm based descriptors selection method. J Chem Inf Comput Sci 43(3) 1077-1084... 

Kamrani AK (2011) Genetic-algorithm-based solution for combinatorial optimization problems. In Kamrani AK, Azimi M (eds) Systems engineering tools and methods. CRC Press, Boca Raton... 

Parameters describing crystallization were optimized using the genetic algorithm inverse method—a stochastic optimization method based on the mechanism of natural selection [52]. 

To overcome the limitations of the database search methods, conformational search methods were developed [95,96,109]. There are many such methods, exploiting different protein representations, objective function tenns, and optimization or enumeration algorithms. The search algorithms include the minimum perturbation method [97], molecular dynamics simulations [92,110,111], genetic algorithms [112], Monte Carlo and simulated annealing [113,114], multiple copy simultaneous search [115-117], self-consistent field optimization [118], and an enumeration based on the graph theory [119]. 

In the present study, we propose a tuning method for PID controllers and apply the method to control the PBL process in LG chemicals Co. located in Yeochun. In the tuning method proposed in the present work, we first find the approximated process model after each batch by a closed-loop Identification method using operating data and then compute optimum tuning parameters of PID controllers based on GA (Genetic Algorithm) method. 

---