Multithreaded Parallel Dual Population Genetic Algorithm (MPDPGA) for unconstrained function optimizations on multi-core system

Various problems viz. population diversity problem, premature convergence problem and curse of dimensionality problem, are associated with Genetic Algorithm (GA). Dual Population GA (DPGA) helps to provide additional population diversity to the main population by means of crossbreeding between the main population and reserve population. This helps to solve the problem of premature convergence and helps in early convergence of the algorithm. The binary encoded Multithreaded Parallel DPGA (MPDPGA) is proposed in this paper to solve the problems of population diversity and premature convergence. The experimental results show that, the performance (mean, standard deviation and standard error of mean), student t-test, mean function evaluation and success rate of MPDPGA is better than serial DPGA (SDPGA) and simple GA (SGA).