Implementation of Local Search in Multi-Objective Adaptive GA: A Case Study on High-Level Synthesis

Choong, F.; Phon-Amnuaisuk, S.; Alias, M. Y.
August 2009
International Journal of Computational Intelligence Research;2009, Vol. 5 Issue 3, p311
Academic Journal
Case Study
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.


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