ACHIYA ELYASAF

Deep-learning-based genetic operators can substantially improve evolutionary search quality, but they also introduce significant computational and energy overhead, bringing the Red AI vs. Green AI tension into evolutionary computation. In this work, we study the energy footprint of Deep Neural Crossover, a reinforcement-learning-based multi-parent crossover operator, and evaluate parameter-level strategies to reduce its power consumption without sacrificing solution quality. We profile experiments with process-level energy measurements and analyze two optimization levers: training batch size and a new fitness-based scheduling threshold that triggers backpropagation only after meaningful fitness improvement. Experiments on benchmark Bin-Packing and Graph Coloring instances show that changing batch size alone has little effect on total energy, whereas the proposed scheduling strategy yields substantial savings. In particular, using larger scheduling thresholds considerably reduces energy consumption while keeping fitness close to the best-performing configurations and, in several cases, matching or improving it. These results provide a practical pathway for greener neuro-evolutionary algorithms through adaptive training schedules.