A New Adaptive Genetic Algorithm and Its Application in the Layout problem

Genetic algorithm (GA) is a search algorithm based on the theory of Darwin. For the purpose of improving the convergent rate and maintaining the population diversity in GA, this paper presents a new genetic operator called trisecting group and directional selection mechanism (TDGA), in which the worst 2/3 of parent individuals are removed from the population before other manipulations. With 1/3 individuals that are selected randomly from the removed parent individuals, the best 1/3 of the parent individuals is manipulated to reproduce offspring. Simulation results based on 10 test functions show that TDGA is feasible and effective. In addition, inspired by the graph of the function f (x) = e(-xc), a new self-adaptation adjusting the tactics of crossover operator and mutation operator (SAGA) is proposed so that individuals with higher fitness cross each other with smaller values of crossover probability, and individuals with lower fitness cross each other with larger values of crossover probability. Combining the two improvements, TA-SAGA is applied to study the layout of drilling equipment in semi- submersible drilling platforms. In addition, the simulated best centroid transverse deviator just only is 0.120m, which is far less than the allowable value 0.7m.