Evolutionary Computation (EC) approaches are inspired by nature and solve optimization
problems in a stochastic manner. They can offer a reliable and effective approach to address …

In data mining, the classical association rule mining techniques deal with binary attributes;
however, real-world data have a variety of attributes (numerical, categorical, Boolean). To …

Many instance selection algorithms have been introduced to reduce the high storage
requirements and computation complexity of K-nearest neighbor (K-NN) classification rules …

We leverage what are typically considered the worst qualities of deep learning algorithms-
high computational cost, requirement for large data, no explainability, high dependence on …

This paper proposes a novel wavefront-shaping-based focusing method, by introducing the
differential evolution algorithm (DEA), thereby realising a faster convergence rate and …

In this paper, a methodological data condensation approach for reducing tabular big
datasets in classification problems is presented, named FDR 2-BD. The key of our proposal …

This study focused, the stator and rotor slot permeance coefficient values of an induction
motor with a 3-phase squirrel cage are optimized using the Average Differential Evolution …

Evolutionary algorithms are inspired by the biological model of evolution and natural
selection and are used to solve computationally hard problems, also known as NP-hard …

We propose a machine learning approach to address a specific algorithmic question that
arises during the solving process of a mixed-integer linear programming problem, namely …

A new bio-inspired, lizard algorithm (LA) is proposed for optimisation of soft computing used
in data mining. Here, an effort has been made to mimic the anole lizard behaviour to …