An experimental approach to design deterministic and adaptive control schemes for grouping genetic algorithms - Flores-Torres, Leonardo; Quiroz-Castellanos, Marcela; Ramos-Figueroa, Octavio; Amador-Larrea, Stephanie (Neural computing & applications, 2025)
Genetic algorithms can solve many complex problems, including designing and optimizing machine learning techniques like neural networks, as well as challenges in production management and engineering. Paradoxically, the design of these methods, which aim to solve optimization problems efficiently, depends, in turn, on their components’ design and optimal configuration. Grouping genetic algorithms (GGAs) have excelled in performance and adaptability as one of the best metaheuristics for solving combinatorial optimization problems that require finding optimal partitions of sets of items; however, their performance relies heavily on the proper configuration of parameters like population size, crossover rate, and mutation rate. This paper presents an experimental approach for automated parameter control of GGAs, looking for a dynamic adjustment, enhancing the algorithm’s ability to explore the solution space efficiently, avoid premature convergence, and improve overall solution quality. A comprehensive set of deterministic and adaptive control schemes is introduced for on-line parameter setting in GGAs. The approach is tested by studying three state-of-the-art algorithms for solving complex instances of three NP-hard optimization problems: the Grouping Genetic Algorithm with Controlled Gene Transmission for the One-Dimensional Bin Packing Problem, the Grouping Genetic Algorithm with Intelligent Heuristic Strategies for the Parallel-Machine Scheduling Problem with Unrelated Machines and Makespan Minimization, and the Grouping Genetic Algorithm for Variable Decomposition in Large-Scale Constrained Optimization Problems. The experimental results showed that the proposed approach allows for identifying parameter control schemes that save the extensive task of off-line parameter fine-tuning, obtaining a robust and competitive performance on different benchmark sets, and outperforming the published results for some classes of instances.