Improved Differential Evolution based Feature Selection through Quantum, Chaos, and Lasso

Modern deep learning continues to achieve outstanding performance on an astounding variety of high-dimensional tasks. In practice, this is obtained by fitting deep neural models to all the input data with minimal feature engineering, thus sacrificing interpretability in many cases. However, in applications such as medicine, where interpretability is crucial, feature subset selection becomes an important problem. Metaheuristics such as Binary Differential Evolution are a popular approach to feature selection, and the research literature continues to introduce novel ideas, drawn from quantum computing and chaos theory, for instance, to improve them. In this paper, we demonstrate that introducing chaos-generated variables, generated from considerations of the Lyapunov time, in place of random variables in quantum-inspired metaheuristics significantly improves their performance on high-dimensional medical classification tasks and outperforms other approaches. We show that this chaos-induced improvement is a general phenomenon by demonstrating it for multiple varieties of underlying quantum-inspired metaheuristics. Performance is further enhanced through Lasso-assisted feature pruning. At the implementation level, we vastly speed up our algorithms through a scalable island-based computing cluster parallelization technique.

Quantum-Inspired Evolutionary Algorithms for Feature Subset Selection: A Comprehensive Survey

The clever hybridization of quantum computing concepts and evolutionary algorithms (EAs) resulted in a new field called quantum-inspired evolutionary algorithms (QIEAs). Unlike traditional EAs, QIEAs employ quantum bits to adopt a probabilistic representation of the state of a feature in a given solution. This unprecedented feature enables them to achieve better diversity and perform global search, effectively yielding a tradeoff between exploration and exploitation. We conducted a comprehensive survey across various publishers and gathered 56 papers. We thoroughly analyzed these publications, focusing on the novelty elements and types of heuristics employed by the extant quantum-inspired evolutionary algorithms (QIEAs) proposed to solve the feature subset selection (FSS) problem. Importantly, we provided a detailed analysis of the different types of objective functions and popular quantum gates, i.e., rotation gates, employed throughout the literature. Additionally, we suggested several open research problems to attract the attention of the researchers.