Crossover Operator

Crossover operators are fundamental components of evolutionary algorithms, aiming to combine genetic material from parent solutions to generate fitter offspring. Current research focuses on developing more efficient and adaptable crossover methods, including those leveraging deep learning architectures (like encoder-decoder networks and recurrent neural networks) and those tailored to specific problem representations (e.g., permutations, graphs, or Boolean functions). These advancements improve the performance of evolutionary algorithms across diverse optimization problems, impacting fields ranging from combinatorial optimization and genetic programming to equation discovery and engineering applications.