A hybrid evolutionary algorithm for Multi-FPGA systems design

Abstract

Genetic algorithms (GAs) are stochastic optimization heuristics in which searches in solution space are carried out by imitating the population genetics stated in Darwin's theory of evolution. The compact genetic algorithm (cGA) does not manage a population of solutions but only mimics its existence. The combination of genetic and local search heuristic has been shown to be an effective approach to solve some optimization problems more efficiently than with a single GA or a cGA. multi-FPGA systems design flow has three major tasks: partitioning, placement and routing. In this paper we present a new hybrid algorithm that exploits a cGA in order to generate high quality partitioning and placement solutions and, by means of a local search heuristic, improves the solutions obtained using a cGA or a GA.