Efficient Evolutionary Optimization Using Individual-Based Evolution Control and Neural Networks: A Comparative Study.

To reduce the number of expensive fitness function evaluations in evolutionary optimization, several individual-based and generation-based evolution control methods have been suggested. This paper compares four individual-based evolution control frameworks on three widely used test functions. Feedforward neural networks are employed for fitness estima- tion. Two conclusions can be drawn from our simulation results. First, the pre-selection strategy seems to be the most stable individual-based evolu- tion control method. Second, structure optimization of neural networks mostly improves the performance of all compared algorithms.