A Constraint-Handling Method for Model-Building Genetic Algorithm: Three-Population Scheme
Journal
Communications in Computer and Information Science
Series/Report No.
Communications in Computer and Information Science
Journal Volume
2828 CCIS
Start Page
24
End Page
36
ISSN
1865-0929
1865-0937
ISBN (of the container)
978-303215634-1
ISBN
9783032156341
9783032156358
Date Issued
2026-02-01
Author(s)
Abstract
To solve constrained optimization problems (COPs) with genetic algorithms, different methods have been proposed to handle constraints, but none of them are specifically designed for model-building genetic algorithms (MBGAs). This paper presents a three-population scheme, abbreviated as B-3Pop, which features three populations: a feasible one, an infeasible one, and a third one to explore the boundary between the feasible and infeasible spaces with MBGAs. The core idea is to learn how to combine feasible and infeasible solutions to evolve optimal solutions near the boundary. Empirically, B-3Pop outperforms five widely used constraint-handling methods—elimination, dominance concept, penalization, adaptive segregational constraint-handling methods, and the feasible-infeasible two-population scheme—in terms of the number of function evaluations on all six tested COPs: dimensional knapsack, uncapacitated warehouse location, Steiner tree, capacitated minimum spanning tree, capacitated p-median, and weighted maximum-2-satisfiability problems.
Event(s)
17th International Joint Conference on Computational Intelligence, IJCCI 2025
Subjects
Constrained Optimization
Model-Building Genetic Algorithms
Publisher
Springer Nature Switzerland
Type
conference paper