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Multi-Objective Evolutionary Neural Network to Predict Graduation Success at the United States Military Academy
Procedia Computer Science
  • Gene Lesinski
  • Steven Corns, Missouri University of Science and Technology

This paper presents an evolutionary neural network approach to classify student graduation status based upon selected academic, demographic, and other indicators. A pareto-based, multi-objective evolutionary algorithm utilizing the Strength Pareto Evolutionary Algorithm (SPEA2) fitness evaluation scheme simultaneously evolves connection weights and identifies the neural network topology using network complexity and classification accuracy as objective functions. A combined vector-matrix representation scheme and differential evolution recombination operators are employed. The model is trained, tested, and validated using 5100 student samples with data compiled from admissions records and institutional research databases. The inputs to the evolutionary neural network model are used to classify students as: graduates, late graduates, or non-graduates. Results of the hybrid method show higher mean classification rates (88%) than the current methodology (80%) with a potential savings of $130M. Additionally, the proposed method is more efficient in that a less complex neural network topology is identified by the algorithm.

Meeting Name
Complex Adaptive Systems Conference with Theme: Cyber Physical Systems and Deep Learning, CAS 2018 (2018: Nov. 5-7, Chicago, IL)
Engineering Management and Systems Engineering
Keywords and Phrases
  • Enrollment management,
  • Evolutionary Algorithms,
  • Multi-objective Evolutionary Algorithms,
  • Neural network,
  • Student retention
Document Type
Article - Conference proceedings
Document Version
Final Version
File Type
© 2019 The Authors, All rights reserved.
Creative Commons Licensing
Creative Commons Attribution-Noncommercial-No Derivative Works 4.0
Publication Date
Citation Information
Gene Lesinski and Steven Corns. "Multi-Objective Evolutionary Neural Network to Predict Graduation Success at the United States Military Academy" Procedia Computer Science Vol. 140 (2018) p. 196 - 205 ISSN: 1877-0509
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