Effects of the use of non-geometric binary crossover on evolutionary multiobjective optimization
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Evaluation of crossover operator performance in genetic algorithms with binary representation
ICIC'11 Proceedings of the 7th international conference on Intelligent Computing: bio-inspired computing and applications
| Hi-index | 0.00 |
The worst one-max solver competition task in GECCO 2007 was to develop a one-max solver that can find the optimal solution of the 15-bit one-max problem as late as possible within 1000 generations. There are two conflicting issues in developing such a one-max solver. One is to slow down the evolution of solutions toward the optimal solution (i.e., not to find the optimal solution in early generations). The other is to find the optimal solution in a very late generation. In this paper, we examine the effect of using a non-geometric binary crossover operator through computational experiments on the worst one-max solver competition task.