Optimization Techniques for Neural Network Architectures and Training

Optimization of neural networks encompasses both the selection of architectural components and the tuning of training procedures to achieve robust performance, fast convergence and efficient resource use. Architectural optimisation spans automated neural architecture search, pruning strategies, quantisation and modular design, while training optimisation covers gradient‐based methods, second‐order approaches and a growing suite of metaheuristic and surrogate‐assisted algorithms. Advances in surrogate modelling allow expensive objectives to be approximated by auxiliary networks, reducing the cost of hyperparameter sweeps and weight initialisation. Hybrid techniques combine stochastic gradient descent with population‐based searches to escape local minima and mitigate overfitting. Automated hyperparameter optimisation and topology design leverage evolutionary and immunocomputing principles to discover novel configurations, with direct impact on applications in medical imaging, autonomous systems, climate modelling and financial forecasting. The current landscape is characterised by tight integration of model‐based search and data‐driven adaptation, yielding architectures that balance accuracy, interpretability and computational footprint.

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Optimization Techniques for Neural Network Architectures and Training publication trend

The graph below shows the total number of articles in optimization techniques for neural network architectures and training across all publications each year (not limited to Nature Index journals).

Technical terms

Metaheuristic algorithm: A high‐level, problem‐independent framework that guides subordinate heuristics to explore complex search spaces for near‐optimal solutions.

Backpropagation: A gradient‐based method for adjusting neural network weights by propagating the error derivative from output to input layers.

Hyperparameter: A parameter whose value is set before the learning process begins and governs aspects such as learning rate, population size or network depth.

Surrogate model: An auxiliary predictive model that approximates an expensive objective function to reduce the number of direct evaluations.

Clonal selection algorithm: An optimisation technique inspired by the adaptive immune system, which iteratively selects, clones and mutates high‐affinity candidates.

Global optimisation: The process of finding the best overall solution in the presence of multiple local optima.

Overfitting: The phenomenon where a model captures noise or idiosyncrasies of the training data, leading to poor generalisation on unseen examples.

References

1. Training Artificial Neural Networks Using a Global Optimization Method That Utilizes Neural Networks. AI (2023).
2. Multi-Layer Perceptron Training Optimization Using Nature Inspired Computing. IEEE Access (2022).
3. Immunocomputing-Based Approach for Optimizing the Topologies of LSTM Networks. IEEE Access (2021).