Evolutionary programming integrating 3-generation based mutation and local competition based selection

Evolutionary programming is mainly characterized by its mutation and selection rules. This paper first proposes a new mutation method that utilizes all the three generations in a family. A child regards its parent as a good or a bad teacher depending on whether it did better or worse than its grandparent. According to this outcome, the child creates a grandchild in the same or the opposite direction to the previous search direction. This 3-generation method allows the use of the gradient information in the search process. Second, contrary to the usual global competition, local competition which takes place in the subgroups of the whole population allows some inferior solutions with a good future potential to survive for enhanced diversity. Benchmark tests reveal the excellent performance of the proposed algorithm