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Neural-Architecture-Search-Based Multiobjective Cognitive Automation System

Currently, deep-learning-based cognitive automation for decision-making in industrial informatics is a new hot topic in the field of cognitive computing, among which multiobjective architecture optimization is of great difficulty in the research area. When the existing algorithms face multiobjective cognitive model problems, it often takes a lot of time to continuously set different search preference parameters to generate a new search process. This article mainly aims to solve the problem in a multiobjective neural architecture search process, and the key issue is how to adapt user preferences during architectural search. We propose a new algorithm: linear-prefer coevolutionary algorithm. Compared to the original user-constrained method and the Pareto-dominant NSGA-II algorithm, we have faster adaptation time and better quality of adaptation. At the same time, it can respond to user's needs at a relatively faster pace during the reasoning phase. Based on a large number of comparative test results, our algorithm is superior to the traditional cognitive automation algorithms for the multiobjective problem in search quality.