📮 Bio-inspired Algorithms in Machine Learning by Balasubramaniam S(.PDF)

Currently, Computational Intelligence approaches are utilised in various science and engineering applications to analyse information, make decisions, and achieve optimisation goals. Over the past few decades, various techniques and algorithms have been created in disciplines such as genetic algorithms, artificial neural networks, evolutionary algorithms, and fuzzy algorithms. In the coming years, intelligent optimisation algorithms are anticipated to become more efficient in addressing various issues in engineering, scientific, medical, space, and artificial satellite fields, particularly in early disease diagnosis. A metaheuristic in Computer Science is designed to discover optimisation algorithms capable of solving intricate issues. Metaheuristics are optimisation algorithms that mimic biological behaviours of animals or birds and are utilised to discover the best solution for a certain problem. Integrating bio-inspired algorithms with Machine Learning (ML) and Deep Learning (DL) models enhances Computational Intelligence (CI). These algorithms, like neural networks modeling the human brain, ant colony optimization, and particle swarm optimization, offer robust, efficient, and flexible models. Their inherent parallelism, adaptability, and self-organization capabilities significantly improve ML/DL model design, accuracy, and generalizability. Genetic algorithms optimize neural networks and hyperparameters, while swarm intelligence identifies optimal solutions, aiding DL model training.

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