A constructive genetic algorithm for gate matrix layout problems

This paper describes an application of a Constructive Genetic Algorithm (CGA) to the gate matrix layout problem (GMLP). The GMLP happens in very large scale integration design and can be described as a problem of assigning a set of circuit nodes (gates) in an optimal sequence, such that the layout area is minimized, as a consequence of optimizing the number of tracks necessary to cover the gates interconnection. The CGA has a number of new features compared to a traditional genetic algorithm. These include a population of dynamic size composed of schemata and structures and the possibility of using heuristics in structure representation and in the fitness function definitions. The application of CGA to GMLP uses a 2-Opt-like heuristic to define the fitness functions and the mutation operator. Computational tests are presented using available instances taken from the literature.