Abstract
Machine-coded genetic algorithms (MCGAs) use the byte representation of floating-point numbers which are encoded in the computer memory. Use of the byte alphabet makes classical crossover operators directly applicable in the floating-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 fields of the search space and performing a local fine-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.
Highlights
Genetic Algorithms (GAs) are search and optimization methods that mimic the natural selection and the principles of genetics [16, 10]
If the initial mutation probability Pm is set to a higher value, the task of exploration is more prominent than the task of exploitation because the algorithm tends to jump many different areas of the seach space frequently, as the t increases by time, the Pm(t) decreases and the task of exploitation becomes more prominent and the population converges to the real solution
Machine-coded genetic operators are applied on the byte representation of candidate solutions on the computer memory
Summary
Genetic Algorithms (GAs) are search and optimization methods that mimic the natural selection and the principles of genetics [16, 10]. Q where L is the length of choromosome b With using this formula, a conversion between binary strings and real numbers in a predened range is possible. This process yields another kind of problems in GAs, that is, as the bit strings get longer, longer number of iterations are required to obtain the global optima. In FPGAs (Floating-point genetic algorithms), the candidate solutions are not encoded using a binary alphabet and the new solutions are recombined and altered using a different logic. Compilers and interpreters encode numbers (including oating-point types) using constant size byte strings using a standard Assuming these byte strings as the geno-type makes the classical crossover operators applicable on the candidate solutions which are vectors of real numbers.
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