Adaptive Species Conserving Genetic Algorithm
This paper introduces an adaptive species conservation genetic algorithm by defining a species with three parameters: species seed, species radius and species boundary fitness. A species is defined as a group of individuals that have similar characteristics and that are dominated by the best individual in the species, called the species seed. Species radius defines the species’ upper boundary and the species boundary fitness is the lowest value of fitness in the boundary. Some heuristic algorithms have been developed to adjust these parameters and an adaptive species conservation genetic algorithm (ASCGA) has been proposed to solve multimodal optimization problems. With heuristic techniques, ASCGA can automatically adjust species parameters and allow the species to adapt to an optimization problem. Experimental results presented demonstrate that the proposed algorithm is capable of finding the global and local optima of test multimodal optimisation problems with a higher efficiency than methods from the literature. ASCGA has also successfully found a significantly different solution of a 25-bar space truss design and identified 761 local solutions of the 2D Shubert function.
| begin Initialize G(t); Evaluate G(t); while (not termination condition) do Conserve species Seeds; Select G(t+1) ; Crossover G(t+1); Mutate G(t+1); Evaluate G(t+1); Adjust species ; end (while) Extract global solutions end |
Parameter Settings:
