Effective memetic algorithms for VLSI design automation = genetic algorithms + local search + multi-level clustering

Authors:
Shawki Areibi;Zhen Yang
Affiliations:
School of Engineering, University of Guelph, Guelph, Ontario, N1G 2W1, Canada;School of Engineering, University of Guelph, Guelph, Ontario, N1G 2W1, Canada
Venue:
Evolutionary Computation - Special issue on magnetic algorithms
Year:
2004

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Abstract

Combining global and local search is a strategy used by many successful hybrid optimization approaches. Memetic Algorithms (MAs) are Evolutionary Algorithms (EAs) that apply some sort of local search to further improve the fitness of individuals in the population. Memetic Algorithms have been shown to be very effective in solving many hard combinatorial optimization problems. This paper provides a forum for identifying and exploring the key issues that affect the design and application of Memetic Algorithms. The approach combines a hierarchical design technique, Genetic Algorithms, constructive techniques and advanced local search to solve VLSI circuit layout in the form of circuit partitioning and placement. Results obtained indicate that Memetic Algorithms based on local search, clustering and good initial solutions improve solution quality on average by 35% for the VLSI circuit partitioning problem and 54% for the VLSI standard cell placement problem.