  Neural Architecture Search aims at automatically finding neural architectures
that are competitive with architectures designed by human experts. While recent
approaches have achieved state-of-the-art predictive performance for image
recognition, they are problematic under resource constraints for two reasons:
(1)the neural architectures found are solely optimized for high predictive
performance, without penalizing excessive resource consumption, (2) most
architecture search methods require vast computational resources. We address
the first shortcoming by proposing LEMONADE, an evolutionary algorithm for
multi-objective architecture search that allows approximating the entire
Pareto-front of architectures under multiple objectives, such as predictive
performance and number of parameters, in a single run of the method. We address
the second shortcoming by proposing a Lamarckian inheritance mechanism for
LEMONADE which generates children networks that are warmstarted with the
predictive performance of their trained parents. This is accomplished by using
(approximate) network morphism operators for generating children. The
combination of these two contributions allows finding models that are on par or
even outperform both hand-crafted as well as automatically-designed networks.
