Genetic Parallel Programming (GPP)
(K. S. Leung and K. H. Lee)

Genetic Programming (GP) is a robust method in Evolutionary Computation. There are two main streams in GP, namely, Tree-based GP (TGP) and Linear GP (LGP). TGP evolves programs represented in tree structure. LGP evolves sequential programs directly. This project is to perform a study on the factors causing the inefficiency of LGP and develop techniques to improve the performance by introducing Genetic Parallel Programming (GPP). In GPP, multiple instructions will be evolved and executed in parallel. It is interesting to find that GPP does not only evolve parallel programs for a parallel architecture but also search more efficiently for a feasible program in the parallel program space than its sequential counterpart. The implementation on FPGA and applications on data mining are also investigated.