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 most of the real-life problems, we are dealing with multiple contradicting objectives, instead of …

We propose a multi-objective variant of AutoML-Zero, called MOAZ, that distributes solutions on a front by trading off the accuracy and computational complexity of the machine learning algorithms. In addition to generating different Pareto-optimal solutions, MOAZ can effectively explore the sparse search space to improve search efficiency.

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.

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.

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.

AZ and MOAZ. In this subsection, we are showing one represen-tative algorithm for each of the frameworks. The AZ solution is shown in Figure 6 and the MOAZ solution is presented in Figure 7. There are not many differences between these representative algo-rithms, apart from the initialization in the Setup component. MOAZ

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. Share on Twitter Facebook LinkedIn Previous Next

2023年7月12日 · In this paper, we propose AutoML for Model Compression (AMC) which leverages reinforcement learning to efficiently sample the design space and can improve the model compression quality. We achieved...

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In this paper, for the newly proposed two-dimensional (2D) Janus MoAZ 3 H (A = Si, or Ge; Z = N, P, or As) monolayer (ML) materials, we theoretically explore the valleytronic and piezoelectric properties using first-principles calculations.