Genetic algorithms (GAs) are population‐based search optimization techniques that mimic the process of evolution and natural selection. GAs are an effective way of finding feasible solutions to complex problems. Recently, GAs were applied to the labeling diversity (LD) problem that produced local optimal mappers for M‐QAM, M‐PSK, and M‐APSK constellations. However, the GA did not implement biological processes during the mating process; hence it could not be classified as a GA, but rather a genetic‐inspired algorithm (GIA). In this article, we investigate the application of six distinct crossover techniques to further improve the GIAs ability to produce more closer‐to‐optimal mapper designs. By using biological crossover techniques, offspring produced acquires genetic diversity from parent chromosomes. Single parent crossover techniques do not produce diversified offspring, thus diversity being acquired only by mutation. The Davis ordered crossover (OX1) was chosen as a suitable technique to be applied to the proposed GA. The proposed GA was tested on 16QAM, 16PSK, 16APSK, and three 16APSK constellations that do not exhibit diagonal symmetry. In the case of the 16QAM and 16PSK constellations, the proposed GA matched but did not improve upon existing mapper designs. In the case of the 16APSK and 11+5APSK constellations, the proposed GA produced mapper designs exhibited diversity gains of ≈0.5 dB over the existing GIA mapper designs at the BER of 10−6. The asymmetric 16APSK and single symmetry 16APSK constellations exhibited diversity gains of ≈4 and ≈2 dB over existing GIA mapper designs at the BER of 10−3 and 10−5. More significantly, the proposed GA achieved a significant improvement in time complexity. However, the proposed GA was shown to have higher computational complexity of O(M!) over the GIA with a complexity of O(M2). Mutation rate studies have shown that for single‐parent crossover techniques, higher mutation rates are needed, thus making the process is purely random. In the case of the two‐parent crossover technique, randomness is reduced and genetic diversity is increased, hence lower mutation rates were required due to a more guided search.