Evolutionary Algorithms in Computer-Aided Design of Integrated Circuits

An increasing number of successful applications of Evolutionary Algorithms (EAs) within Computer Aided Design (CAD) of Integrated Circuits (ICs) is reported in the literature. The problems dealt with in this eld consist of sequences of sub-problems, which are characterized by being NP-hard, large and mutually dependent. This very high level of complexity should make the EA a well suited approach, at least in principle. However, the EA is of practical interest to the CAD community if and only if it is competitive to the existing approaches with respect to performance. With this fact as the starting point, the purpose of this paper is to discuss how to develop high-performance EAs for CAD of ICs. After reviewing a number of recent, performance competitive EAs for key problems in CAD of ICs, the common characteristics of these algorithms are discussed. They all exploit problem speciic knowledge in various ways or are integrated with other heuris-tics. The paper also discusses performance evaluation principles. To make an impact, it is crucial that performance is evaluated using the measures commonly applied in the CAD eld. The practical implications hereof for EA-based applications are addressed. Finally, we discuss a model for EAs to learn heuris-tics starting from a given set of basic operations. The diierence to other previous applications of EAs in CAD of ICs is that the EA does not solve the problem directly. Rather, it developes strategies for solving the problem.

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