Real-coded genetic algorithm with variable rates of cross-over and mutation: a basis of global optimization for multi-modal functions via interval technique

Impacts of Genetic Algorithm Parameters on the Solution Performance for the Uniform Circular Antenna Array Pattern Synthesis Problem

“…In addition to the constant mutation rate approach of the classical genetic algorithm, increasing and decreasing mutation rates are examined in this study. As mentioned in previous studies, such as [12] and [18], results show that a decreasing mutation rate along with the generations outperform constant and increasing mutation rates. Nevertheless, an increasing mutation rate along with the generations reduce the performance (i.e.…”

“…-Even though in some previous publications (such as [12] and [18]), it has been mentioned that a decreasing mutation rate yields better performance, to our knowledge, there exists no publication explicitly explaining how to decrease the mutation rate ideally. From this aspect, the paper comes up with an answer to such a question.…”

“…The effect of mutation and crossover on the GA performance has been the focus of attention of many researchers since the beginning of the 1990s. There are currently a considerable number of studies in the literature on this subject [10][11][12][13][14][15][16][17][18][19][20][21][22][23]. In [10], adaptive crossover and mutation probabilities have been realized in order to maintain the diversity in the population while, at the same time, sustaining the convergence capacity of the GA.…”

“…In [14][15][16], the adaptive mutation rate was used for improvement in the GA performance. The floating point crossover and the mutation approach were discussed in [17]; some GA variants (hybridized or parallelized versions) with variable mutation and crossover rate have also been proposed and applied for optimization of bound constrained nonlinear multi-modal functions in [18][19][20]. Studies conducted with similar motivations but concerning the mutation rates in genetic programming are also available in the literature (such as [21]).…”

A Genetic Algorithm Based Hybrid Approach for Reliability-Redundancy Optimization Problem of a Series System with Multiple-Choice

“…But there is no hard and fast rule to choose the values of all parameters. From the literature (Goldberg et al, 1989;Michalewicz and Schoenauer, 1996;Jabeen and Bhunia, 2006), it is seen that, if the values of the parameters are not chosen in the reasonable range, there arise some difficulties. If the value of size Pop is taken very large then the computational cost is large and also storing of data in computer in intermediate steps of GA may arise some difficulties at the time of computation.…”