Using Variants of Genetic Algorithm and Learnable Evolution Model to Solve Resource-Constrained Project Scheduling Problem
| dc.contributor.author | Dr. Gamal Alshorbagy | |
| dc.contributor.author | Dr. Mohamed El-dosuky | |
| dc.date.accessioned | 2025-12-18T09:49:00Z | |
| dc.date.issued | 2025-07 | |
| dc.description.abstract | The resource-constrained project scheduling problem (RCPSP) is a complex scheduling challenge as it is proven to be NP-hard. The Learnable Evolution Model (LEM) is a non-Darwinian evolutionary approach that speeds up convergence using machine learning instead of crossover. It classifies individuals into high-performance (H-group) and low-performance (L-group) based on fitness, learns distinguishing features, and generates new individuals through an instantiation step. To ensure diversity, LEM applies mutation as a Darwinian component, making it more efficient than traditional evolutionary methods. This paper proposes a new approach, which attempts variants of genetic algorithms and LEM, aiming to tackle issues in generating Gantt charts for big cases. | |
| dc.identifier.uri | https://research.arabeast.edu.sa/handle/123456789/494 | |
| dc.language.iso | en | |
| dc.publisher | Journal of Theoretical and Applied Information Technology | |
| dc.title | Using Variants of Genetic Algorithm and Learnable Evolution Model to Solve Resource-Constrained Project Scheduling Problem | |
| dc.type | Article |