Abstract
This paper suggests a new framework of multidimensional genetic algorithm and applies it to the real-world problem of very large scale integration (VLSI) partitioning. The framework consists of a new multidimensional genetic operator, called geographic crossover, and a new genetic encoding scheme. Geographic crossover enables more powerful creation of new solutions by allowing a diverse mixture of parent solutions. Its theoretical validity is proved based on a new view of crossover. The new genetic encoding scheme helps space search by effectively utilizing geographical linkages of genes. The new framework can be incorporated into most existing genetic algorithm (GA) implementations just by replacing the crossover module and leaving the other modules intact. For a test suite of 11 ACM/SIGDA VLSI circuit␣partitioning benchmark circuits, the GA under this framework significantly outperformed recently published state-of-the-art methods as well as a previous GA on linear string.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
