An Adaptive Grouping Genetic Algorithm with Controlled Gene Transmission Based on Fullness and Item Strategies (AGGA-CGT-FIS)

The one-dimensional Bin Packing Problem (1D-BPP) is a well-known NP-hard grouping problem characterized by high structural complexity and broad practical relevance. Among the metaheuristic approaches proposed for this problem, the Grouping Genetic Algorithm with Controlled Gene Transmission (GGA-CGT) has shown remarkable performance. In this work, an Adaptive Grouping Genetic Algorithm with Controlled Gene Transmission based on Fullness and Item Strategies (AGGA-CGT-FIS) is presented. This approach extends the original GGA-CGT by integrating domain-guided crossover mechanisms and adaptive parameter control schemes. The proposed algorithm incorporates a novel gene-level crossover operator, termed Fullness–Items Gene-Level Crossover 1 (FI-GLX-1). This operator exploits structural information from the solutions through Fullness- and Item-based ordering and transmission strategies. In addition, adaptive control schemes are introduced for key evolutionary parameters associated with crossover and mutation. These mechanisms allow the algorithm to dynamically adjust its behavior according to feedback extracted from the search process, resulting in a fully adaptive variant of the GGA-CGT. The effectiveness of AGGA-CGT-FIS is evaluated using two benchmark sets for the 1D-BPP: the classic and the BPPvu_c instances. The proposed approach is compared against the baseline GGA-CGT using the original Gene-Level Crossover (GLX) operator. Experimental results show improvements in solution quality and convergence behavior, supported by statistical analyses that confirm the significance of the observed performance differences.