A Hybrid Genetic Algorithm with Information-Theoretic Local Search for Unsupervised Feature Selection

Hybridizing Genetic Algorithms with Pareto Local Search for Multi-Objective Rule Learning

This work investigates the hybridization of a Genetic Algorithm with Pareto Local Search to improve the exploration of non-dominated rulesets and provides a documented baseline for hybrid metaheuristics in rule mining.

Multiscale-adaptive and Size-adaptive PSO-based Feature Selection for Gene Expression Analysis

Differential evolution with fitness-based mutation strategy and diversity enhancement mechanism for numerical optimization

A Comparative Analysis of Evolutionary Metaheuristics: Genetic Algorithm vs. Particle Swarm Optimization for Feature Selection in High-Accuracy Fake News Detection

The proliferation of online misinformation demands the development of highly accurate and computationally efficient automated systems for Fake News Detection. A primary impediment to system performance is the high dimensionality of textual features derived from techniques like TF-IDF, making optimal Feature Selection a critical step. This paper presents a detailed comparative experimental study of two prominent bio-inspired evolutionary metaheuristics, the Genetic Algorithm (GA) and Particle Swarm Optimisation (PSO) used as wrapper-based FS techniques for FND. The methodologies were rigorously tested across two distinct textual datasets: the complex, large-scale FakeNewsNet corpus and a moderate-scale general news dataset. The feature sets, once optimised, were evaluated using six standard Machine Learning (ML) classifiers. The GA-based FS approach, emphasising global exploration, achieved state-of-the-art accuracy of 99.91% with the Random Forest classifier on the FakeNewsNet dataset. In contrast, the PSO-based FS approach, valued for its rapid convergence, yielded a maximum accuracy of 93.29% with the Support Vector Machine (SVM) on the general news dataset. This analysis provides empirical evidence of the intrinsic trade-off between the algorithms: GA is superior for maximising accuracy in high-dimensional, complex textual spaces, while PSO offers a more efficient and practical solution for resource-constrained or moderate-scale FND tasks. The study confirms that evolutionary computation provides a robust, effective pathway for significantly enhancing ML classifier performance in this critical domain.

When Frequency Fitness Assignment Fails: Trapped States in Frequency-Guided Local Search

Frequency Fitness Assignment (FFA) offers an alternative take on metaheuristic optimization. Here, the encounter frequencies of objective values are used to make the selection decisions. This leads to a variety of interesting algorithm features, such as an invariance under all injective transformations of the objective function value and a very strong focus on exploration of the search space. In this article, for the first time, we discover a condition under which purely FFA-guided search can actually get stuck, even on a problem as simple as OneMax. To tackle this issue, we suggest hybrid approaches combining objective-guided and FFA-guided search. We propose using crossover for solution transfer between the two component algorithms of the hybrids. Our experiments show that (1) the original FFA allows us to solve problems like Trap, TwoMax, and Jump in (experimentally observed) polynomial time; (2) the suggested hybrids address FFA's shortcomings and are occasionally orders of magnitude faster; and (3) we report several new best-known solutions for the NP-hard low-autocorrelation binary sequences problem.

Supervised Feature Selection via Collective First-Order Neural Dynamics.

Feature selection is a core machine learning technique for selecting the most informative features. Most existing feature selection methods rely on greedy strategies, leading to solutions. Although some global feature selection methods improve classification performance, their effectiveness remains limited due to category information or inaccurate relevance measurements. In this article, the feature selection task is reformulated as a mixed-integer optimization problem that minimizes feature redundancy and maximizes feature relevance. Given the nonconvex nature of this formulation, a collective first-order neural dynamics (CFND) method is proposed to solve the optimization problem. The method combines first-order neural dynamics (FND) with a collective mechanism to substantially enhance the global search capability. Theoretical analyses prove that the proposed CFND method converges globally with probability 1. Extensive experiments across ten benchmark datasets demonstrate that the proposed CFND method consistently outperforms other feature selection methods across all classifiers, achieving an average accuracy improvement of approximately 2%-5%. Compared with traditional methods, the maximum performance gain exceeds 6%. These findings validate that the synergy between FND and the collective mechanism effectively balances local exploitation and global exploration, leading to robust feature selection performance across diverse datasets and parameter settings. The source code is available at https://github.com/LongJin-lab/CFND.