A Local Optima Network Analysis of the Feedforward Neural Architecture Space

• URL: http://arxiv.org/abs/2206.06903v1
• Date: Thu, 2 Jun 2022 08:09:17 GMT
• Title: A Local Optima Network Analysis of the Feedforward Neural Architecture Space
• Authors: Isak Potgieter, Christopher W. Cleghorn and Anna S. Bosman
• Abstract summary: Local optima network (LON) analysis is a derivative of the fitness landscape of candidate solutions. LONs may provide a viable paradigm by which to analyse and optimise neural architectures.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: This study investigates the use of local optima network (LON) analysis, a derivative of the fitness landscape of candidate solutions, to characterise and visualise the neural architecture space. The search space of feedforward neural network architectures with up to three layers, each with up to 10 neurons, is fully enumerated by evaluating trained model performance on a selection of data sets. Extracted LONs, while heterogeneous across data sets, all exhibit simple global structures, with single global funnels in all cases but one. These results yield early indication that LONs may provide a viable paradigm by which to analyse and optimise neural architectures.

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