Fuzzy genetic algorithm for floorplanning

Genetic algorithms (GAs) have been found to be very effective in solving nu merous optimization problems, especially those with many (possibly) conflic ting and noisy objectives. However, there seems to be no consensus as to wh at fitness measure to use in such situations, and how to rank individuals i n a population on the basis of several conflicting objectives. Fuzzy logic provides an effective and easy way of dealing with such class of problems. In this work, we present a fuzzy genetic algorithm (FGA), which combines th e parallel and robust search properties of GA with the expressive power of fuzzy logic. In the proposed FGA, the fitness of individuals is evaluated b ased on fuzzy logic rules expressed on linguistic variables modeling the de sired objective criteria of the problem domain. Several fitness fuzzificati on approaches are evaluated and compared with Weighted Sum GA (WS-GA), wher e the fitness is set equal to a weighted sum of the objective criteria. Exp erimental evaluation was conducted using as a testbed the floorplanning of Very Large Scale Integrated (VLSI) circuits.