Digital natives have natural skills of using technology. In spite of that skill, their role, as the producer of digital world, is an arguing point of this study. In order to discuss that point, a training course for children, "Programming with Small Basic", was designed. The study group is consisted of nineteen children, aged 11 to 14. Dominant-less dominant quantitative-qualitative sequential mixed research method was used in the study. Collected data were analysed by using descriptive analysis, content analysis and Wilcoxon test (SPSS 21.0). The results show that digital natives are good readers of digital world but they need to be supported to become good writers of that world.
In this paper, we extend the Compact Genetic Algorithm (CGA) for real-valued optimization problems by dividing the total search process into three stages. In the first stage, an initial vector of probabilities is generated. The initial vector contains the probabilities of bits having 1 depending on the bit locations as defined in the IEEE-754 standard. In the second stage, a CGA search is applied on the objective function using the same encoding scheme. In the last stage, a local search is applied using the result obtained by the previous stage as the starting point. A simulation study is performed on a set of well-known test functions to measure the performance differences. Simulation results show that the improvement in search capabilities is significant for many test functions in many dimensions and different levels of difficulty.
Machine-coded genetic algorithms (MCGAs) use the byte representation of oating-point numbers which are encoded in the computer memory. Use of the byte alphabet makes classical crossover operators directly applicable in the oating-point genetic algorithms. Since effect of the byte-based mutation operator depends on the location of the mutated byte, the byte-based mutation operator mimics the functionality of its binary counterpart. In this paper, we extend the MCGA by developing new type of byte-based genetic operators including a random mutation and a random dynamic mutation operator. We perform a simulation study to compare the performances of the byte-based operators with the classical FPGA operators using a set of test functions. The prepared software package, which is freely available for downloading, is used for the simulations. It is shown that the byte-based genetic search obtains precise results by carrying out the both exploration and exploitation tasks by discovering new elds of the search space and performing a local ne-tuning. It is also shown that the introduced byte-based operators improve the search capabilities of FPGAs by means of convergence rate and precision even if the decision variables are in larger domains.
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