Doctoral Dissertations

Keywords and Phrases

Deep Learning; Genetic Algorithm; Neural Architecture Search; Optimization


"A long-standing goal in Deep Learning (DL) research is to design efficient architectures for a given dataset that are both accurate and computationally inexpensive. At present, designing deep learning architectures for a real-world application requires both human expertise and considerable effort as they are either handcrafted by careful experimentation or modified from a handful of existing models. This method is inefficient as the process of architecture design is highly time-consuming and computationally expensive.

The research presents an approach to automate the process of deep learning architecture design through a modeling procedure. In particular, it first introduces a framework that treats the deep learning architecture design problem as a systems architecting problem. The framework provides the ability to utilize novel and intuitive search spaces to find efficient architectures using evolutionary methodologies. Secondly, it uses a parameter sharing approach to speed up the search process and explores its limitations with search space. Lastly, it introduces a multi-objective approach to facilitate architecture design based on hardware constraints that are often associated with real-world deployment.

From the modeling perspective, instead of designing and staging explicit algorithms to process images/sentences, the contribution lies in the design of hybrid architectures that use the deep learning literature developed so far. This approach enjoys the benefit of a single problem formulation to perform end-to-end training and architecture design with limited computational resources"--Abstract, page iii.


Dagli, Cihan H., 1949-

Committee Member(s)

Qin, Ruwen
Kwasa, Benjamin J.
Yin, Zhaozheng
Wunsch, Donald C.


Engineering Management and Systems Engineering

Degree Name

Ph. D. in Systems Engineering


Missouri University of Science and Technology

Publication Date

Spring 2020


xi, 80 pages

Note about bibliography

Includes bibliographic references (pages 77-79).


© 2020 Ram Deepak Gottapu, All rights reserved.

Document Type

Dissertation - Open Access

File Type




Thesis Number

T 11677

Electronic OCLC #