Ultra-dense networks (UDNs) are a defining feature of 6G: thousands of small cells and devices share a limited spectrum, so co-channel interference rather than noise limits performance. Assigning channels to cells or users in such networks is a combinatorial, NP-hard problem whose search space grows exponentially with network size. Genetic algorithms (GAs) offer powerful global search, but they repeatedly evaluate an expensive interference-based fitness function and react slowly to changing traffic. Neural networks (NNs) offer millisecond-scale inference and can learn traffic and interference patterns, but they need large training sets and give no guarantee that constraints are satisfied. Combining the two is a natural but still fast-moving research direction. This survey (i) formalises the interference-aware channel assignment problem, (ii) proposes a taxonomy of five GA–NN hybridisation patterns (surrogate-assisted, predict-then-evolve, neuroevolution, GA-labelled or seeded NN, and NN-guided operators), (iii) reviews more than 40 works spanning classical and evolutionary channel/spectrum assignment, neural and graphbased channel allocation, deep reinforcement learning, and evolutionary-computation foundations, drawing its evidence base specifically from work published between 2015 and 2025 so that the survey reflects the current, AI-native direction of 6G research, and (iv) synthesises nine research gaps, a research problem statement, and a reference framework for future work. We conclude that scalable, constraintaware, multi-objective hybrid GA–NN methods evaluated on common benchmarks remain largely open for 6G UDNs, even as the first dedicated GA–DRL hybrids for 6G spectrum sharing and resource allocation.

Khushbu Patle, Dr Divya Rai, "Evolutionary–Neural Hybrids for Interference-Aware Channel Assignment in Ultra-Dense 6G Networks: A Survey, Taxonomy, and Research Roadmap" International Journal of Scientific Research and Engineering Development, V9(5): Page(559-566) September - October 2026. ISSN: 2581-7175. www.ijsred.com. Published by Scientific and Academic Research Publishing.