Genetic Algorithm Optimization
optimization is a significant improvement over the traditional
hill-climb optimization technique that has been employed by TRANSYT-7F
for many years. The genetic algorithm has the ability to avoid
becoming trapped in a "local optimum" solution, and is designed to
locate the "global optimum" solution. TRANSYT-7F features
genetic algorithm optimization of cycle length, phasing sequence, splits, and offsets.
Now, with genetic algorithm optimization, the engineer
have a more difficult time in coming up with a better solution than the
computer program. The genetic algorithm does not
examine every single timing plan candidate either, but is a
random guided search, capable of intelligently tracking down
the global optimum solution. As with the human race, the weakest
candidates are eliminated from the gene pool, and each successive
of individuals contains stronger and stronger characteristics.
Itís survival of the fittest, and the unique processes of crossover
and mutation conspire to keep the species as strong
Although it produces the
best timing plans, a practical disadvantage of the genetic algorithm
involves longer running times on the computer. Fortunately, this disadvantage continues to be minimized
the ever-increasing processing speeds of today's computers. In
addition, the TRANSYT-7F documentation offers practical suggestions for
reducing optimization running times.
Take the "G.A. Challenge": After recording hundreds of test cases, no input file has ever been found for which hill-climb optimization produces a better timing plan than genetic algorithm optimization. If you encounter a TRANSYT input file for which hill-climb optimization appears to produce a better result, submit it to McTrans technical support by e-mail. We will attempt to return it genetic-optimized with the best solution.