On Comparison between Evolutionary Programming Network-based Learning and Novel Evolution Strategy Algorithm-based Learning
This work addresses the problem of optimizing neural network training and architecture for medical diagnosis, but it is incremental as it compares existing evolutionary methods.
The paper compares two evolutionary learning systems, EPNet and NES, on medical diagnosis benchmarks like breast cancer, diabetes, and heart disease, showing their performance differences.
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.