Genetic algorithm parameters

In common with most other AI algorithms, the GA contains several variables whose values are chosen at the start of a run. Decisions must also be made about how to implement the evolutionary operators within the algorithm because there may be more than one way in which the operators can be used. We shall deal with the permissible values of these parameters and the factors that help us to choose among the evolutionary operators as they are introduced. 

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The Genetic Algorithm Parameters Used in the Dipoles Problem... 

Genetic Algorithm Parameters. For the considered here model parameter identification, the type of the basic operators in GA are as follows ... 

Table 1. Comparison of Genetic Algorithm parameter estimation with values from Todic et al. [14]...

Table 2. Multiobjective genetic algorithm parameters and rules.

Pereira, R.M., Clerc, F., Farrusseng, D., Waal, J.C. and Maschmeyer, T. (2005). Effect of the genetic algorithm parameters on the optimisation of heterogeneous catalysts, QSAR Comb. Sci., 24, 45-57. 

Figure B3.2.1. The band structure of hexagonal GaN, calculated using EHT-TB parameters detemiined by a genetic algorithm [23]. The target energies are indicated by crosses. The target band structure has been calculated with an ab initio pseudopotential method using a quasiparticle approach to include many-particle corrections [194].

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. 

To determine the optimal parameters, traditional methods, such as conjugate gradient and simplex are often not adequate, because they tend to get trapped in local minima. To overcome this difficulty, higher-order methods, such as the genetic algorithm (GA) can be employed [31,32]. The GA is a general purpose functional minimization procedure that requires as input an evaluation, or test function to express how well a particular laser pulse achieves the target. Tests have shown that several thousand evaluations of the test function may be required to determine the parameters of the optimal fields [17]. This presents no difficulty in the simple, pure-state model discussed above. 

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. 

Friese, T. P. Ulbig and S. Schulz. Use of Evolutionary Algorithms for the Calculation of Group Contribution Parameters in Order to Predict Thermodynamic Properties. Part 1 Genetic Algorithms. Comput Chem Eng 22 1559-1572 (1998). 

Huang and Tang49 trained a neural network with data relating to several qualities of polymer yarn and ten process parameters. They then combined this ANN with a genetic algorithm to find parameter values that optimize quality. Because the relationships between processing conditions and polymer properties are poorly understood, this combination of AI techniques is a potentially productive way to proceed. 

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