Proceedings ArticleDOI
Channel and switchbox routing with minimized crosstalk. A parallel genetic algorithm approach
Jens Lienig
- pp 27-31
TLDR
A novel approach to solve the VLSI channel and switchbox routing problems with the objective of satisfying crosstalk constraints for the nets is presented, based on a parallel genetic algorithm which runs on a distributed network of workstations.Abstract:
Reduction of crosstalk between interconnections becomes an important consideration in today's VLSI design. This paper presents a novel approach to solve the VLSI channel and switchbox routing problems with the objective of satisfying crosstalk constraints for the nets. The approach is based on a parallel genetic algorithm which runs on a distributed network of workstations. All our routing results are qualitatively better or as good as the best published results. In addition, our algorithm is able to significantly reduce the occurrence of crosstalk.read more
Citations
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Proceedings ArticleDOI
A multi-layer detailed routing approach based on evolutionary algorithms
TL;DR: An evolutionary algorithm for detailed routing problems (DRPs), like the channel routing problem and the switchbox routing problem, that combines EAs with domain specific knowledge, i.e. the genetic operators make use of the rip-up and reroute technique.
Book ChapterDOI
Physical Design of VLSI Circuits and the Application of Genetic Algorithms
TL;DR: The task of VLSI physical design is to produce the layout of an integrated circuit and a specific parallel genetic algorithm is presented for the routing problem in V LSI circuits.
Book ChapterDOI
Incremental Knowledge Acquisition for Improving Probabilistic Search Algorithms
TL;DR: A new incremental knowledge acquisition approach for the effective development of efficient problem solvers for combinatorial problems based on probabilistic search algorithms by incrementally building a knowledge base that controls parts of the Probabilistic algorithm.
References
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Book
Genetic algorithms in search, optimization, and machine learning
TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
Genetic algorithms in search, optimization and machine learning
TL;DR: This book brings together the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields.