Novelty Driven Evolutionary Neural Architecture Search

• URL: http://arxiv.org/abs/2204.00188v1
• Date: Fri, 1 Apr 2022 03:32:55 GMT
• Title: Novelty Driven Evolutionary Neural Architecture Search
• Authors: Nilotpal Sinha, Kuan-Wen Chen
• Abstract summary: Evolutionary algorithms (EA) based neural architecture search (NAS) involves evaluating each architecture by training it from scratch, which is extremely time-consuming. We propose a method called NEvoNAS wherein the NAS problem is posed as a multi-objective problem with 2 objectives: (i) maximize architecture novelty, (ii) maximize architecture fitness/accuracy. NSGA-II is used for finding the textitpareto optimal front for the NAS problem and the best architecture in the pareto front is returned as the searched architecture.
• Score: 6.8129169853808795
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Evolutionary algorithms (EA) based neural architecture search (NAS) involves evaluating each architecture by training it from scratch, which is extremely time-consuming. This can be reduced by using a supernet for estimating the fitness of an architecture due to weight sharing among all architectures in the search space. However, the estimated fitness is very noisy due to the co-adaptation of the operations in the supernet which results in NAS methods getting trapped in local optimum. In this paper, we propose a method called NEvoNAS wherein the NAS problem is posed as a multi-objective problem with 2 objectives: (i) maximize architecture novelty, (ii) maximize architecture fitness/accuracy. The novelty search is used for maintaining a diverse set of solutions at each generation which helps avoiding local optimum traps while the architecture fitness is calculated using supernet. NSGA-II is used for finding the \textit{pareto optimal front} for the NAS problem and the best architecture in the pareto front is returned as the searched architecture. Exerimentally, NEvoNAS gives better results on 2 different search spaces while using significantly less computational resources as compared to previous EA-based methods. The code for our paper can be found in https://github.com/nightstorm0909/NEvoNAS.

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