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|dc.title||Handling constraints in multi-objective GA for embedded system design|
|dc.identifier.citation||Chakraborty, B.,Chen, T.,Mitra, T.,Roychoudhury, A. (2006). Handling constraints in multi-objective GA for embedded system design. Proceedings of the IEEE International Conference on VLSI Design 2006 : 305-310. ScholarBank@NUS Repository. <a href="https://doi.org/10.1109/VLSID.2006.95" target="_blank">https://doi.org/10.1109/VLSID.2006.95</a>|
|dc.description.abstract||Design space exploration is central to embedded system design. Typically this is a multi-objective search problem, where performance, power, area etc. are the different optimization criteria, to find the Pareto-optimal points. Multi-objective Genetic Algorithms (GA) have been found to be a natural fit for such searches and have been used widely. However, for certain design spaces, a large part of the space being explored by GA may violate certain design constraints. In this paper, we use a multi-objective GA algorithm based on "repair", where an infeasible design point encountered during the search is repaired to a feasible design point. Our primary novelty is to use a multi-objective version of search algorithms, like branch and bound, as the repair strategy to optimize the objectives. We also precompute a layout of the genes such that infeasible design points are less likely to be encountered during the search. We have successfully employed our hybrid search strategy to design application-specific instruction-set extensions that maximize performance and minimize area. © 2006 IEEE.|
|dc.contributor.department||STATISTICS & APPLIED PROBABILITY|
|dc.description.sourcetitle||Proceedings of the IEEE International Conference on VLSI Design|
|Appears in Collections:||Staff Publications|
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