TRANSYT-7F features genetic algorithm optimization of cycle length, phasing sequence, splits, and offsets. The genetic algorithm has the ability to avoid becoming trapped in a “local optimum” solution, and is designed to locate the “global optimum” solution. The hill-climb optimization option is also available within the software, for the purpose of quickly obtaining a reasonable and effective timing plan. The genetic algorithm allows advanced searches that the hill-climb method can’t handle, such as phasing sequence optimization, multi-period optimization, grouped node optimization, uncoordinated optimization, and direct CORSIM optimization.
The genetic algorithm 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 generation of individuals contains stronger and stronger characteristics. It’s survival of the fittest, and the unique processes of crossover and mutation conspire to keep the species as strong as possible.
The genetic algorithm tends to require longer running times on the computer when compared to other optimization techniques. However, the genetic algorithm can be easily customized by specifying the “number of generations”. To quickly obtain a reasonable timing plan, specify a small number of generations. To obtain the global optimum solution, specify a large number of generations.