# Complexity Analysis and Application of Gray Code-Based Genetic Algorithm for Container Loading Problem Optimization > Akbar M. URL kanonis: https://discover.unhas.ac.id/publications/pub_scopus_105013464308 Jurnal / Konferensi: Proceedings 2025 4th International Conference on Electronics Representation and Algorithm Artificial Intelligence Creating Tomorrow S World Today Icera 2025 Tahun terbit: 2025 DOI: https://doi.org/10.1109/ICERA66156.2025.11087304 Citations: 0 ## Authors - Akbar M. ## Abstract The Container Loading Problem involves arranging boxes of varying sizes and weights into constrained three-dimensional spaces. This study employs a Genetic Algorithm (GA) enhanced with Gray Code (GC) encoding for more stable and efficient optimization. GC reduces disruptions during mutation and crossover, ensuring smoother evolution. The proposed method was evaluated on fifty simulated datasets with randomly generated dimensions and weights. Complexity was analyzed theoretically and empirically by comparing execution times to various Complexity models, including linear, linear-logarithmic, quadratic, and cubic. Results revealed that the cubic model best represents the experimental data, emphasizing high computational demands, particularly during placement. The GC-GA achieved a superior ${R}^{{2}}$ value of 0.9769, outperforming the Original and Binary GA, thus highlighting enhanced performance and scalability. This confirms the effectiveness of integrating GC within GA frameworks for solving complex, high-dimensional optimization tasks like Container Loading. ## Keywords - Computer science - Gray (unit) - Gray code - Container (type theory) - Genetic algorithm - Algorithm - Code (set theory) - Mathematical optimization - Mathematics - Engineering - Programming language - Machine learning - Mechanical engineering - Medicine - Radiology - Set (abstract data type) --- Sumber: Discover Unhas — RIMS Universitas Hasanuddin. Saat mengutip, gunakan DOI bila tersedia atau URL kanonis di atas.