EvoNet: Evolutionary Synthesis of Deep Neural Networks. In this study, we introduce the idea of synthesizing new highly efficient, yet powerful deep neural networks via a novel evolutionary process from ancestor deep neural networks. The architectural genetics of ancestor deep neural networks is encapsulated using the concept of synaptic probability density models, which can be viewed as the ’DNA’ of these ancestor networks. These synaptic probability density models from the ancestor networks are then leveraged, along with computational environmental factor models to synthesize new descendant deep neural networks with different network architectures in a random manner to mimic natural selection and random mutations. These ’evolved’ deep neural networks (which we will term EvoNets) are then trained into fully functional networks, like one would train a newborn, and have more efficient, more varied synapse architectures than their ancestor networks, while achieving powerful modeling capabilities. Experimental results using the MSRA-B dataset for the purpose of image segmentation was performed, and it was demonstrated that the synthesized EvoNets can achieve state-of-the-art F_beta score (0.872 at second generation, 0.859 at third generation, and 0.839 at fourth generation) while having synapse architectures that are significantly more efficient ( 19X fewer synapses by the fourth generation) compared to the original ancestor deep neural network.