  We propose a novel approach for the challenge of designing less complex yet
highly effective convolutional neural networks (CNNs) through the use of
cartesian genetic programming (CGP) for neural architecture search (NAS). Our
approach combines real-based and block-chained CNNs representations based on
CGP for optimization in the continuous domain using multi-objective
evolutionary algorithms (MOEAs). Two variants are introduced that differ in the
granularity of the search space they consider. The proposed CGP-NASV1 and
CGP-NASV2 algorithms were evaluated using the non-dominated sorting genetic
algorithm II (NSGA-II) on the CIFAR-10 and CIFAR-100 datasets. The empirical
analysis was extended to assess the crossover operator from differential
evolution (DE), the multi-objective evolutionary algorithm based on
decomposition (MOEA/D) and S metric selection evolutionary multi-objective
algorithm (SMS-EMOA) using the same representation. Experimental results
demonstrate that our approach is competitive with state-of-the-art proposals in
terms of classification performance and model complexity.
