2023年7月12日 · We propose a multi-objective variant of AutoML-Zero called MOAZ, that distributes solutions on a Pareto front by trading off accuracy against the computational complexity of the machine learning algorithm. In addition to generating different Pareto-optimal solutions, MOAZ can effectively explore the sparse search space to improve search efficiency.

We have named the new framework Multi-Objective AZ (MOAZ). In this paper, we demonstrate that making the search algorithm multi-objective yields two improvements over AZ: 1) the complexity of the resulting algorithms becomes much smaller compared to algorithms discovered by AZ, and 2) the success rate of the algorithm search improves significantly.

MOAZ is multi-objective extension of AutoML-Zero. Unlike AutoML-Zero, MOAZ can handle multiple objectives while searching for machine learning models in an automated fashion. In …

2023年12月1日 · Biaxial strain have a profound impact on photocatalytic properties of Janus XMoAZ 2. With regards to the asymmetry in its structure, two-dimensional Janus materials exhibit distinctive properties when compared to symmetric structures, which endows them with vast potential for application.

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MOAZ: A Multi-Objective AutoML-Zero Framework. Published in Genetic and Evolutionary Computation Conference (GECCO), 2023 Ritam Guham, Wei Ao, Stephen Kelly, Vishnu Boddeti, Erik Goodman, Wolfgang Banzhaf and Kalyanmoy Deb GECCO 2023

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2023年11月8日 · In this paper, based on the newly discovered MoA 2 Z 4 (A = Si, Ge; Z = N, P, As) ML, we propose a class of 2D Janus MoAZ 3 H ML materials with good stability and excellent mechanical properties using first-principles calculations.

MOAZ solution initializes just the two layers in the neural network but AZ solution initializes some other vectors as well. Please note that these are discovered algorithms with more than 99% accuracy.

In this paper, based on the newly discovered MoA2Z4 (A = Si, Ge; Z = N, P, As) ML, we propose a class of 2D Janus MoAZ3H ML materials with good stability and excellent mechanical properties using first-principles calculations.