1University Tunku Abdul Rahman, Faculty of Engineering and Science, Jalan Genting Kelang, 53300 Setapak, Kuala Lumpur, Malaysia E-mail: florence.choong@gmail.com.
2Multimedia University, Faculty of Information Technology, Jalan Multimedia, 63100 Cyberjaya, Malaysia E-mail: somnuk.amnuaisuk@mmu.edu.my.
3Multimedia University, Faculty of Engineering, Jalan Multimedia, 63100 Cyberjaya, Malaysia. E-mail: yusoff@mmu.edu.m
Although there are many characteristics of Genetic Algorithms (GAs) which qualify them to be a robust based search procedure, still GAs are not well suited to perform finely tuned search. One way to improve performance of GAs is through inclusion of local search, creating a hybrid genetic algorithm (HGA). The inclusion of local search helps to speed up the solution process and to make the solution technique more robust. A high-level synthesis framework based on hybrid evolutionary computation is presented. This novel hybrid evolutionary computation algorithm includes two levels of optimization: a stochastic global search method using a multi-objective adaptive genetic algorithm and a local optimization technique to create a hybrid adaptive GA (HAGA). By using this method, a desirable convergence of solutions has been accomplished by applying a controllable search strategy.
Genetic Algorithm; High-level Synthesis, adaptive, local search, hybrid
