Genetic algorithms have been shown to be very useful in a variety of search and optimization problems. In this paper, we propose a modified genetic channel router. We adopt the compatible crossover operator and newly designed compatible mutation operator in order to search solution space more effectively, where vertical constraints are integrated. By carefully selected fitness function forms and optimized genetic parameters, the current version speeds up benchmarks on average about 5.83 times faster than that of our previous version. Moreover the total convergence to optimal solutions for benchmarks can be always obtained.

Genetic algorithms have been shown to be very useful in a variety of search and optimization problems. In this paper, we describe the implementation of genetic algorithms for channel routing problems and identify the key points which are essential to making full use of the population of potential solutions, that is one of the characteristics of genetic algorithms. Three efficient crossover techniques which can be divided further into 13 kinds of crossover operators have been compared. We also extend our previous work with ability to deal with dogleg case by simply splitting multi-terminal nets into a series of 2-terminal subnets. It routes the Deutsch's difficult example with 21 tracks without any detours.

Evolution programs have been shown to be very useful in a variety of search and optimization problems, however, until now, there has been little attempt to apply evolution programs to channel routing problem. In this paper, we present an exolution program and identify the key points which are essential to successfully applying evolution programs to channel routing problem. We also indicate how integrating heuristic information related to the problem under consideration helps in convergence on final solutions and illustrate the validity of out approach by providing experimental results obtained for the benchmark tests. compared with the optimal solutions.