Efficient method of input variable partitioning in functional decomposition based on evolutionary algorithms

In recent years the functional decomposition has found an application in many fields of modern engineering and science, such as combinational and sequential logic synthesis for VLSI systems, pattern analysis, knowledge discovery, machine learning, decision systems, data bases, data mining etc. However, the lack of an effective and efficient method of the input variable partitioning limits its practical usefulness in complex systems. A classical method based on a systematic search of the whole solution space is inefficient due to its nonpolynomial time complexity. In this paper, a heuristic method for the input variable partitioning is proposed and discussed. The method is based on the application of evolutionary algorithms that allows exploring the possible solution space of a problem while keeping the high-quality solutions in this reduced space. The experimental results show that the proposed heuristic method is able to construct an optimal or near optimal solution very efficiently even for large systems. It is much faster than the systematic method while delivering results of comparable quality.