Study on pre-evolution genetic algorithm

Genetic algorithm excels at astringency and robustness when used in single static function optimization. But due to it's time consuming operation, it can hardly be used in such applications which needs high real timing, and the optimization objects may change dynamically. In order to enable the genetic algorithm to be dynamic, to fit the dynamic decision optimal object and, consequently to optimize it, we propose the 'pre-evolution genetic algorithm'. The basic idea is, by the help of the technology of parallel computation, to foster the good individuals for all possible during the process of deciding the optimization object. Once the specific object is decided, it can be optimized quickly on the basis of fostered individuals. In this paper, the realization method of the algorithm and the algorithm's verification is introduced. In the end we testify the validity the algorithm through samples. It is found that it takes the traditional Genetic algorithm around iterating 300 generations to find a good solution for a single function, while it takes about 150 generations for the 'pre-evolution' Genetic algorithm, based on the simultaneous evolution for every functions, and about iterating 50 generations for a single function. Because the 'pre-evolution' is run together with decision-making process, so this algorithm can quickly find the good solution for a function after it is selected.