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This paper presents two different evolutionary systems - EvolutionaryProgramming Network (EPNet) and Novel Evolutions Strategy (NES) Algorithm.EPNet does both training and architecture evolution simultaneously, whereas NESdoes a fixed network and only trains the network. Five mutation operatorsproposed in EPNet to reflect the emphasis on evolving ANNs behaviors. Closebehavioral links between parents and their offspring are maintained by variousmutations, such as partial training and node splitting. On the other hand, NESuses two new genetic operators - subpopulation-based max-mean arithmeticalcrossover and time-variant mutation. The above-mentioned two algorithms havebeen tested on a number of benchmark problems, such as the medical diagnosisproblems (breast cancer, diabetes, and heart disease). The results and thecomparison between them are also presented in this paper.

On Comparison between Evolutionary Programming Network-based Learning and Novel Evolution Strategy Algorithm-based Learning