Cost function genetic algorithms

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. 

The difficulty with this procedure is that simple refinement routines, such as simplex or least squares, lead only to the nearest minimum in the cost function which is unlikely to be the global minimum. The refinement procedure therefore has to be one that randomly samples different parts of configuration space so as to be able to reach different minima, ultimately selecting the global minimum. Two refinement methods have been proposed, simulated annealing and the genetic algorithm. 

Keywords Genetic algorithms Simulated annealing Structure prediction Cost function ... 

Fig. 4 A typical change in a the cost function (arbitrary units) for the best candidate out of a population of 100 candidates and b the diversity (percent) of the population where a genetic algorithm (GA) is used to predict the structure of BaO with NG=643, =8, Pt=0.9, Pc=0.4 and Pm=0.0 (broken line) or l/l26 (solid line)...

Direct Methods Direct-Space Techniques Patterson Methods Monte Carlo Simulated Annealing Genetic Algorithm Degree of Freedom Cost Function... 

Where n is the number of gray levels in the image and the termp i) represents the normalized fi quency of occurrence for each gray level (each prohahflity is between zero and one, and the sinn of all probabilities is one). Again to solve this minimization problems is comphcated because the cost functions are non-hnear a the constrained conditions are non-hnear and non-convex. That is why the authors used a hybridized form of a modified float genetic algorithm. 

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