The School of Computer Science is pleased to present…
Genetic Algorithm Optimization of Logical-operator Decision Graphs
MSc Thesis Proposal by: Pouria Sadr
Date: Friday October 16, 2026
Time: 10:00 AM
Location: Dillon Hall DH253
Abstract:
Decision diagrams are powerful representations for Boolean functions, but their efficiency depends heavily on the structure of the generated diagram. This thesis investigates the use of genetic algorithms to optimize the construction of Decision Graphs, with the goal of generating more compact representations while preserving exact functionality. In this work, we propose an approach that combines a greedy diagram construction strategy with evolutionary optimization, where a genetic algorithm searches for construction parameters that improve the resulting diagram structure. The generated graphs are evaluated based on structural properties such as size and complexity, while correctness is verified against the original functions. Experimental evaluation will compare the genetic approach with traditional greedy construction methods across different datasets to study when evolutionary optimization can improve graphs compactness and representation quality.
Keywords: Genetic algorithms, Boolean functions, diagram optimization
Thesis Committee:
Internal Reader: Dr. Dan Wu
External Reader: Dr. Ahmed Hamdi Sakr
Advisor: Dr. Luis Rueda
