Novel Discretization Scheme for Multidimensional Split-on-Demand on Real-Valued Optimization with High Multi-modality
Journal
Communications in Computer and Information Science
Journal Volume
2828 CCIS
Start Page
281
End Page
294
ISSN
1865-0929
1865-0937
ISBN (of the container)
978-303215634-1
ISBN
9783032156341
9783032156358
Date Issued
2026-02-01
Author(s)
Liang, Che-Wei
Abstract
Real-valued optimization has attracted considerable interest because of its wide-ranging applications in areas such as control system design, circuit design, and hyperparameter tuning for machine learning. In this paper, we propose the skew multidimensional split-on-demand (smSoD), an extension of multidimensional split-on-demand (mSoD). mSoD serves as a discretization interface that enables discrete model-building genetic algorithms to tackle continuous-domain problems; smSoD further improves split-point selection by leveraging the sample distribution. We then embed smSoD into the integer version of the extended compact genetic algorithm (ECGA) and evaluate it on two benchmark suites: decomposable linkage problems and an extended version of the CEC2014 benchmark with additional subproblems. We compare smSoD+ECGA against bothmSoD+ECGA and L-SHADE. According to statistical tests, under high-dimensional scenarios, our method outperforms the other two methods on more than 50% of the test problems.
Event(s)
17th International Joint Conference on Computational Intelligence, IJCCI 2025
Subjects
Adaptive Discretization
Genetic Algorithm
Real-Valued Optimization
Publisher
Springer Nature Switzerland
Type
conference paper