Doctoral Dissertations


Gene Lesinski

Keywords and Phrases

Computational Intelligence; Hypervolume; Pareto Front; System Architecture; System Design


"Systems Engineering involves the development or improvement of a system or process from effective need to a final value-added solution. Rapid advances in technology have led to development of sophisticated and complex sensor-enabled, remote, and highly networked cyber-technical systems. These complex modern systems present several challenges for systems engineers including: increased complexity associated with integration and emergent behavior, multiple and competing design metrics, and an expansive design parameter solution space. This research extends the existing knowledge base on multi-objective system design through the creation of a framework to explore and analyze system design alternatives employing computational intelligence. The first research contribution is a hybrid fuzzy-EA model that facilitates the exploration and analysis of possible SoS configurations. The second contribution is a hybrid neural network-EA in which the EA explores, analyzes, and evolves the neural network architecture and weights. The third contribution is a multi-objective EA that examines potential installation (i.e. system) infrastructure repair strategies. The final contribution is the introduction of a hierarchical multi-objective evolutionary algorithm (MOEA) framework with a feedback mechanism to evolve and simultaneously evaluate competing subsystem and system level performance objectives. Systems architects and engineers can utilize the frameworks and approaches developed in this research to more efficiently explore and analyze complex system design alternatives"--Abstract, page iv.


Corns, Steven

Committee Member(s)

Long, Suzanna, 1961-
Dagli, Cihan H., 1949-
Raper, Stephen A.
Wunsch, Donald C.
Farr, John


Engineering Management and Systems Engineering

Degree Name

Ph. D. in Systems Engineering


Missouri University of Science and Technology

Publication Date

Fall 2019

Journal article titles appearing in thesis/dissertation

  • A fuzzy genetic algorithm approach to generate and assess meta-architectures for non-line of site fires battlefield capability
  • Multi-objective evolutionary neural network to predict graduation success at the United States Military Academy
  • A Pareto based Multi-Objective Evolutionary Algorithm approach to military installation rail infrastructure investment
  • A hierarchical multi-objective evolutionary algorithm framework to evolve complex system design


xv, 108 pages

Note about bibliography

Includes bibliographic references.


© 2019 Eugene Joseph Lesinski III, All rights reserved.

Document Type

Dissertation - Open Access

File Type




Thesis Number

T 11632

Electronic OCLC #