Thinking Machines

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Complex problems often require solutions that go beyond straightforward programming. The theme “Thinking Machines: Computational Intelligence & Optimization” focuses on computational intelligence techniques—such as evolutionary algorithms, neural networks, swarm intelligence, and optimization methods—that allow machines to learn, adapt, and improve performance over time.

Projects in this theme may involve solving optimization problems, designing adaptive algorithms, or applying intelligent methods to real-world systems in engineering, agriculture, or business. By working on these projects, students gain experience in creating systems that mimic aspects of human or collective intelligence, offering solutions that are efficient, scalable, and innovative.

ALCABASA, Alan Jason C., BSCS 2008
The use of committee machines for insurance risk rating of automobiles
CIMATU, Arthur F., BSCS 2003
Configuring a three-layer feed-forward back-propagation neural network for license plate recognition
DE JESUS, Theodore John F., BSCS 2007
Learning spam: Genetic algorithm vs. backpropagation
DE LEON, Gelanie T., BSCS 2004 (co-advised by Prof. Michael Jonathan M. Mendoza)
Solving the static single runway aircraft landing problem using ant colony algorithm
FUENTES, Charles Christian B., BSCS 2000
A fuzzy logic-based comprehension assessment system
GAGNO, Villamor Jr. O., BSCS 2004 (co-advised by Prof. Michael Jonathan M. Mendoza)
A genetic algorithm approach to the 3D packing problem single container loading
GAMO, Gabriel Joaquin O., BSCS 2020 cum laude
Percentile Pruning: A proposed magnitude-based pruning method to be used while training
LAURON, Maureen Lyndel C., MSCS 2016
Ensemble learning using selected algorithms for classification in data mining
MARINTES, Glenn M., BSCS 2004 (co-advised by Prof. Michael Jonathan M. Mendoza)
Solving the resource constrained shortest path problem using ant colony optimization
REVILLA, Maria Patricia M., BSCS 2004 (co-advised by Prof. Ma. Christine A. Gendrano)
Solving the resource constrained shortest path problem using genetic algorithms
YAP, Ethel Jean P., BSCS 2004 (co-advised by Prof. Ma. Christine A. Gendrano)
Solving the equitable partitioning problem using genetic algorithms

Students are encouraged to contribute to this theme by applying computational intelligence and optimization methods to diverse domains. Whether the project tackles resource allocation, scheduling, predictive modeling, or intelligent decision-making, the student's effort helps advance the development of machines that think, adapt, and solve problems in ways that traditional methods cannot.