Machine learning methods, and particularly deep neural networks, are a rapidly growing field and are currently being employed in domains such as science, business, and government. However, the significant success of neural networks has largely been due to the increasingly large model sizes and enormous amounts of required training data. As a result, powerful neural networks are accompanied by growing storage and memory requirements, making these powerful models infeasible for practical scenarios that use small embedded devices without access to cloud computing.
As such, methods for significantly reducing the memory and computational requirements of high-performing deep neural networks via sparsification and/or compression have been developed. More recently, the concept of evolutionary deep intelligence was proposed, and takes inspiration from nature and allows highly-efficient deep neural networks to organically synthesize over successive generations. However, current work in evolutionary deep intelligence has been limited to the use of asexual evolutionary synthesis where a newly synthesized offspring network is solely dependent on a single parent network from the preceding generation.
In this thesis, we introduce a general framework for synthesizing efficient neural network architectures via multi-parent evolutionary synthesis. Generalized from the asexual evolutionary synthesis approach, the framework allows for a newly synthesized network to be dependent on a subset of all previously synthesized networks. By imposing constraints on this general framework, the cases of asexual evolutionary synthesis, 2-parent sexual evolutionary synthesis, and m-parent evolutionary synthesis can all be realized.
We explore the computational construct used to mimic heredity, and generalize it beyond the asexual evolutionary synthesis used in current evolutionary deep intelligence works. The efficacy of incorporating multiple parent networks during evolutionary synthesis was examined first in the context of 2-parent sexual evolutionary synthesis, then generalized to m-parent evolutionary synthesis in the context of varying generational population sizes. Both experiments show that the use of multiple parent networks during evolutionary synthesis allows for increased network diversity as well as steeper trends in increasing network efficiency over generations.
We also introduce the concept of gene tagging within the evolutionary deep intelligence framework as a means to enforce a like-with-like mating policy during the multi-parent evolutionary synthesis process, and evaluate the effect of architectural alignment during multi-parent evolutionary synthesis. We present an experiment exploring the quantification of network architectural similarity in populations of networks.
In addition, we investigate the the computational construct used to mimic natural selection. The impact of various environmental resource models used to mimic the constraint of available computational and storage resources on network synthesis over successive generations is explored, and results clearly demonstrate the trade-off between computation time and optimal model performance.
The results of m-parent evolutionary synthesis are promising, and indicate the potential benefits of incorporating multiple parent networks during evolutionary synthesis for highly-efficient evolutionary deep intelligence. Future work includes studying the effects of inheriting weight values (as opposed to random initialization) on total training time and further investigation of potential structural similarity metrics, with the goal of developing a deeper understanding of the underlying effects of network architecture on performance
