Evolutionary algorithms and their applications to engineering problems
A Slowik, H Kwasnicka - Neural Computing and Applications, 2020 - Springer
The main focus of this paper is on the family of evolutionary algorithms and their real-life
applications. We present the following algorithms: genetic algorithms, genetic programming …
applications. We present the following algorithms: genetic algorithms, genetic programming …
Multi-objective particle swarm optimization with adaptive strategies for feature selection
F Han, WT Chen, QH Ling, H Han - Swarm and Evolutionary Computation, 2021 - Elsevier
Feature selection is a multi-objective optimization problem since it has two conflicting
objectives: maximizing the classification accuracy and minimizing the number of the …
objectives: maximizing the classification accuracy and minimizing the number of the …
PS-Tree: A piecewise symbolic regression tree
The symbolic methods have recently regained popularity due to their reasonable
interpretability compared to neural network-based artificial intelligence techniques. The …
interpretability compared to neural network-based artificial intelligence techniques. The …
A divide-and-conquer genetic programming algorithm with ensembles for image classification
Genetic programming (GP) has been applied to feature learning in image classification and
achieved promising results. However, one major limitation of existing GP-based methods is …
achieved promising results. However, one major limitation of existing GP-based methods is …
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 …
Learning feature spaces for regression with genetic programming
Genetic programming has found recent success as a tool for learning sets of features for
regression and classification. Multidimensional genetic programming is a useful variant of …
regression and classification. Multidimensional genetic programming is a useful variant of …
[HTML][HTML] Genetic programming for enhanced detection of advanced persistent threats through feature construction
Abstract Advanced Persistent Threats (APTs) pose considerable challenges in the realm of
cybersecurity, characterized by their evolving tactics and complex evasion techniques …
cybersecurity, characterized by their evolving tactics and complex evasion techniques …
Transfer learning in constructive induction with genetic programming
Transfer learning (TL) is the process by which some aspects of a machine learning model
generated on a source task is transferred to a target task, to simplify the learning required to …
generated on a source task is transferred to a target task, to simplify the learning required to …
Cost-sensitive probability for weighted voting in an ensemble model for multi-class classification problems
A Rojarath, W Songpan - Applied Intelligence, 2021 - Springer
Ensemble learning is an algorithm that utilizes various types of classification models. This
algorithm can enhance the prediction efficiency of component models. However, the …
algorithm can enhance the prediction efficiency of component models. However, the …
Slug: Feature selection using genetic algorithms and genetic programming
We present SLUG, a method that uses genetic algorithms as a wrapper for genetic
programming (GP), to perform feature selection while inducing models. This method is first …
programming (GP), to perform feature selection while inducing models. This method is first …