Achieving Multi-objectives Using a Single Neural Network


  • Muhammad Arslan
  • Muhammad Mubeen
  • Giri Anandhi


Achieving multi-objectives, single neural network, Mean Squared Error, stochastic gradient descent, Cross-Entropy, confusion matrix


Achieving multi-objectives using a single neural network is a research area that aims to develop techniques to optimize multiple objectives simultaneously using a single neural network. This approach can be applied in various fields, such as image and speech recognition, robotics, and natural language processing, to achieve better performance and reduce the complexity of the models. The key challenge in this area is to balance the trade-off between different objectives and to develop appropriate algorithms for optimizing multiple objectives. Various techniques have been proposed in the literature, including evolutionary algorithms, reinforcement learning, and multi-objective optimization algorithms. The success of this approach depends on the ability of the neural network to learn to balance different objectives and to generalize to new tasks.