ITM Web Conf.
Volume 59, 2024II International Workshop “Hybrid Methods of Modeling and Optimization in Complex Systems” (HMMOCS-II 2023)
|Number of page(s)
|Adaptive Intelligence: Exploring Learning in Evolutionary Algorithms and Neural Networks
|25 January 2024
Multi-objective parametric synthesis of loss functions in neural networks with evolutionary algorithms
Reshetnev Siberian State University of Science and Technology
31, Krasnoyarskii rabochii prospekt,
2 Siberian Federal University 79, Svobodnii ave., Krasnoyarsk, 660041, Russian Federation
* Corresponding author: email@example.com
The loss function is a fundamental aspect of neural network training and by choosing a suitable one, better results can be achieved. In classification problems, the cross-entropy loss function is almost exclusively used. In this paper the loss function represented by Taylor's series which are optimized with multi-objective evolutionary algorithm. As results show the new loss function can be better than cross-entropy, however application of multi-objective algorithm does not bring an improvement in comparison with single-objective algorithm.
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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