A survey on evolutionary neural architecture search
Deep neural networks (DNNs) have achieved great success in many applications. The
architectures of DNNs play a crucial role in their performance, which is usually manually …
architectures of DNNs play a crucial role in their performance, which is usually manually …
Automatic design of machine learning via evolutionary computation: A survey
Abstract Machine learning (ML), as the most promising paradigm to discover deep
knowledge from data, has been widely applied to practical applications, such as …
knowledge from data, has been widely applied to practical applications, such as …
A survey on evolutionary computation for computer vision and image analysis: Past, present, and future trends
Computer vision (CV) is a big and important field in artificial intelligence covering a wide
range of applications. Image analysis is a major task in CV aiming to extract, analyze and …
range of applications. Image analysis is a major task in CV aiming to extract, analyze and …
A survey on evolutionary construction of deep neural networks
Automated construction of deep neural networks (DNNs) has become a research hot spot
nowadays because DNN's performance is heavily influenced by its architecture and …
nowadays because DNN's performance is heavily influenced by its architecture and …
Lights and shadows in evolutionary deep learning: Taxonomy, critical methodological analysis, cases of study, learned lessons, recommendations and challenges
Much has been said about the fusion of bio-inspired optimization algorithms and Deep
Learning models for several purposes: from the discovery of network topologies and …
Learning models for several purposes: from the discovery of network topologies and …
Recent trends in nature inspired computation with applications to deep learning
Nature-inspired computations are commonly recognized optimization techniques that
provide optimal solutions to a wide spectrum of computational problems. This paper …
provide optimal solutions to a wide spectrum of computational problems. This paper …
Cooperative Coevolutionary Spatial Topologies for Autoencoder Training
Training autoencoders is non-trivial. Convergence to the identity function or overfitting are
common pitfalls. Population based algorithms like coevolutionary algorithms can provide …
common pitfalls. Population based algorithms like coevolutionary algorithms can provide …
Problem Decomposition Strategies and Credit Distribution Mechanisms in Modular Genetic Programming for Supervised Learning
L Rodriguez-Coayahuitl… - IEEE Transactions …, 2025 - ieeexplore.ieee.org
In this review article, we provide a comprehensive guide to the endeavor of problem
decomposition within the field of Genetic Programming (GP), specifically tree-based GP for …
decomposition within the field of Genetic Programming (GP), specifically tree-based GP for …
A genetic programming encoder for increasing autoencoder interpretability
F Schofield, L Slyfield, A Lensen - European Conference on Genetic …, 2023 - Springer
Autoencoders are powerful models for non-linear dimensionality reduction. However, their
neural network structure makes it difficult to interpret how the high dimensional features …
neural network structure makes it difficult to interpret how the high dimensional features …
A framework for designing of genetic operators automatically based on gene expression programming and differential evolution
The design of genetic operators is absolutely one of the core work of evolutionary algorithms
research. However, the essence of the evolutionary algorithms is that a lot of algorithm …
research. However, the essence of the evolutionary algorithms is that a lot of algorithm …