A Surrogate-Assisted Synergistic Co-evolutionary Framework for Computationally Expensive Neural Architecture Search
Neural Architecture Search (NAS), often formulated as a bilevel optimization problem, presents significant challenges characterized by discrete search spaces and computationally expensive performance evaluations. Standard evolutionary metaheuristics frequently encounter difficulties in such landscapes due to ineffici...