  This paper presents two different evolutionary systems - Evolutionary
Programming Network (EPNet) and Novel Evolutions Strategy (NES) Algorithm.
EPNet does both training and architecture evolution simultaneously, whereas NES
does a fixed network and only trains the network. Five mutation operators
proposed in EPNet to reflect the emphasis on evolving ANNs behaviors. Close
behavioral links between parents and their offspring are maintained by various
mutations, such as partial training and node splitting. On the other hand, NES
uses two new genetic operators - subpopulation-based max-mean arithmetical
crossover and time-variant mutation. The above-mentioned two algorithms have
been tested on a number of benchmark problems, such as the medical diagnosis
problems (breast cancer, diabetes, and heart disease). The results and the
comparison between them are also presented in this paper.
