Gender Genetic Algorithm in the Dynamic Optimization Problem
This work addresses dynamic optimization problems, such as extinguishing natural fires, but appears incremental as it builds on existing gender genetic algorithms by incorporating the Boldwin effect.
The paper tackles dynamic optimization problems by introducing a gender genetic algorithm that divides the population into male and female subpopulations, with males undergoing large mutations and strong selection to enhance adaptability, while females fix adaptability, and it shows an advantage in finding optimal solutions through the Boldwin effect compared to traditional methods.
A general approach to optimizing fast processes using a gender genetic algorithm is described. Its difference from the more traditional genetic algorithm it contains division the artificial population into two sexes. Male subpopulations undergo large mutations and more strong selection compared to female individuals from another subset. This separation allows combining the rapid adaptability of the entire population to changes due to the variation of the male subpopulation with fixation of adaptability in the female part. The advantage of the effect of additional individual learning in the form of Boldwin effect in finding optimal solutions is observed in comparison with the usual gender genetic algorithm. As a promising application of the gender genetic algorithm with the Boldwin effect, the dynamics of extinguishing natural fires is pointed.