A New Asynchronous Parallel Evolutionary Algorithm for Function Optimization
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Animating the Evolution Process of Genetic Algorithms
SEAL'98 Selected papers from the Second Asia-Pacific Conference on Simulated Evolution and Learning on Simulated Evolution and Learning
A Method for Model Parameter Identification Using Parallel Genetic Algorithms
Proceedings of the 6th European PVM/MPI Users' Group Meeting on Recent Advances in Parallel Virtual Machine and Message Passing Interface
Evolutionary and adaptive synthesis methods
Formal engineering design synthesis
Evolutionary algorithms for the physical design of VLSI circuits
Advances in evolutionary computing
Computer Networks: The International Journal of Computer and Telecommunications Networking
A genetic algorithm high-level optimizer for complex datapath and data-flow digital systems
Applied Soft Computing
Evolutionary dynamics on scale-free interaction networks
IEEE Transactions on Evolutionary Computation
Parallelizing post-placement timing optimization
IPDPS'06 Proceedings of the 20th international conference on Parallel and distributed processing
Automated antenna design using paralleled differential evolution algorithm
International Journal of Computer Applications in Technology
| Hi-index | 0.00 |
This paper presents a novel approach to solve the VLSI (very large scale integration) channel and switchbox routing problems. The approach is based on a parallel genetic algorithm (PGA) that runs on a distributed network of workstations. The algorithm optimizes both physical constraints (length of nets, number of vias) and crosstalk (delay due to coupled capacitance). The parallel approach is shown to consistently perform better than a sequential genetic algorithm when applied to these routing problems. An extensive investigation of the parameters of the algorithm yields routing results that are qualitatively better or as good as the best published results. In addition, the algorithm is able to significantly reduce the occurrence of crosstalk