Learning heuristics by genetic algorithms

In many applications of Computer Aided Design (CAD) of Integrated Circuits (ICs) the problems that have to be solved are NP-hard. Thus, exact algorithms are only applicable to small problem instances and many authors have presented heuristics to obtain solutions (non-optimal in general) for larger instances of these hard problems. In this paper we present a model for Genetic Algorithms (GA) to learn heuristics starting from a given set of basic operations. The difference to other previous applications of GAs in CAD of ICs is that the GA does not solve the problem directly. Rather, it develops strategies for solving the problem. To demonstrate the efficiency of our approach experimental results for a specific problem are presented.