13th International Conference on Fracture June 16–21, 2013, Beijing, China -4- state-space; whereas for complex and large search state-space, this requires rather a method of stochastic search (genetic algorithm, pattern search ...). The difficulties of these problems via conventional optimization methods give rapidly this family of algorithms able to handle large combinatorial problems with mixed variable. It is more interested for solving practical problems by a general classification of optimization problems and solved methods [2,3]. Briefly, genetic algorithms are adaptive heuristic search algorithm based on the evolutionary idea of natural selection and genetic. Moreover, they are a part of evolutionary computing, a rapidly growing area of artificial intelligence. The strategy proposed in this work is to utilize a combination of the GA with the PSA. We will briefly describe these two algorithms. 3.1 Genetic Algorithm This algorithm starts with the creation of the initial population of individuals and terminates with the convergence towards the best individuals of population giving therefore the optimized solution. The transition from one generation to another is accomplished by applying the following process: (i) mechanism of evaluation, (ii) selection and (iii) modification, up to obtaining a stopping criterion. The structure of this algorithm is given by the flowchart [4] (figure 1). Figure 1 Structure of the genetic algorithm Each individual of a given population is defined by a chain of genes that correspond to the different parameters to be identified. To avoid the difficulties that may arise in the binary coding and decoding of individual, a real coding GA is used [4]. The values of each parameter are bounded by Random initialization of the population Evaluation of the "fitness" of each individual Reached tolerance Recopying the population Classification of individuals from best to worst New population creation by crossover and mutation of individuals of the previous population End optimization Yes no
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