There are various paradigms of learning. One such paradigm is learning from knowledge
assimilation. In machine learning, such a paradigm may be considered where concepts …

Nature-inspired meta-heuristic algorithms have dominated the scientific literature in the
areas of machine learning and cognitive computing paradigm in the last three decades …

The supervised learning methods applying evolutionary algorithms to generate knowledge
model are extremely costly in time and space. Fundamentally, this high computational cost is …

When investigated carefully, chemical reactions possess efficient objects, states, process,
and events that can be designed as a computational method en bloc. In this study, a novel …

The most challenging issue in dealing with big datasets is the large number of their
dimensions. Feature selection is a technique for reducing the dimensionality of datasets by …

Our aim is to find the best catalyst, the best combination of compounds, in order to optimize a
chemical reaction. The chemists use mainly a heuristic algorithm, especially an evolutionary …

Nature inspired algorithms have become popular for discovering classification rules due to
their ability to effectively handle large and complex search spaces. However, nature inspired …

Nowadays, a big pool of different machine learning components (ie, algorithms and tools)
exists that are capable of predicting various decisions in different problem domains …

This paper presents an integrated approach by considering chemical reaction optimization
(CRO) and functional link artificial neural networks (FLANNs) for building a classifier from …

Abstract Chemical Reaction Optimization (CRO) is a recently established population based
metaheuristic for optimization problems inspired by the natural behavior of chemical …