A new real-valued diploid genetic algorithm for optimization in dynamic environments

Many real-world problems are dynamic, requiring an optimization algorithm which is able to continuously track a changing optimum over the time. Using a diploidy and dominance is one method to enhance the performance of genetic algorithms in dynamic environment. Diploid genetic algorithm has two chromosomes in each individual. In this paper, for the first time, a real-valued diploid genetic algorithm is proposed. Its new dominance mechanism is based on a simple function with homogeneous outputs. In addition, a new dominance change mechanism is added to the algorithm. Hence, when environment change occurs, it can increase diversity to respond more quickly to the changes. Other diploid genetic algorithms in literature are discrete and they have never been tested by Moving Peak Benchmark (MPB) which is continuous and dynamic. For the first time, the proposed approach is tested by MPB. Results are compared with other diploid genetic algorithms showing that proposed algorithm significantly outperforms previous approaches.