Swarm Intelligence and Evolutionary Computation: Theory, Advances and Applications in Machine Learning and Deep Learning

Автор: literator от 18-01-2023, 18:41, Коментариев: 0


Swarm Intelligence and Evolutionary Computation: Theory, Advances and Applications in Machine Learning and Deep LearningНазвание: Swarm Intelligence and Evolutionary Computation: Theory, Advances and Applications in Machine Learning and Deep Learning
Автор: Georgios N. Kouziokas
Издательство: CRC Press
Год: 2023
Страниц: 218
Язык: английский
Формат: pdf (true)
Размер: 10.2 MB

The application of Artificial Intelligence (AI) has been greatly developed in many scientific sectors the last years, with the development of new AI-based algorithms and optimization techniques. Swarm intelligence and evolutionary computation are generally utilized as advanced methods to solve several kinds of computational optimization problems especially when there is only a small amount of relevant information.

Swarm intelligence was inspired by studying the swarm behavior of animals in nature. The social intelligence of natural swarm behavior was suitably transformed to develop computational optimization algorithms. Swarm optimization algorithms have a stochastic nature where the swarm individuals are traversing a predefined search space while the swarm algorithm evaluates the individual and the global best fitness function values in every step so as to estimate the next positions of the individuals in the space depending also on predefined hyperparameters and conditions.

Evolutionary computational optimization was inspired by the natural biological evolution. Evolutionary algorithms produce improved optimization solutions in many kinds of optimization problems in several scientific sectors such as: engineering applications, computer science and electrical engineering problems, environmental forecasting, mechanical engineering, education, transportation, scheduling optimization, machine learning and deep learning model optimization.

Machine learning methods use training algorithms to create AI-based prediction models. According to numerous studies in the literature, swarm intelligence and evolutionary computation can be utilized with increased success in order to enhance the predictability and the generalization ability of the machine learning models.

The aim of this book is to present and analyse theoretical advances and also emerging practical applications of swarm and evolutionary intelligence. It comprises nine chapters. Chapter 1 provides a theoretical introduction of the computational optimization techniques regarding the gradient-based methods such as steepest descent, conjugate gradient, newton and quasi-Newton methods and also the non-gradient methods such as genetic algorithm and swarm intelligence algorithms. Chapter 2, discusses evolutionary computation techniques and genetic algorithm. Swarm intelligence theory and particle swarm optimization algorithm are reviewed in Chapter 3. Also, several variations of particle swarm optimization algorithm are analysed and explained such as Geometric PSO, PSO with mutation, Chaotic PSO with mutation, multi-objective PSO and Quantum mechanics – based PSO algorithm. Chapter 4 deals with two essential colony bio-inspired algorithms: Ant colony optimization (ACO) and Artificial bee colony (ABC). Chapter 5, presents and analyses Cuckoo search and Bat swarm algorithms and their latest variations. In chapter 6, several other metaheuristic algorithms are discussed such as: Firefly algorithm (FA), Harmony search (HS), Cat swarm optimization (CSO) and their improved algorithm modifications.

The latest Bio-Inspired Swarm Algorithms are discussed in chapter 7, such as: Grey Wolf Optimization (GWO) Algorithm, Whale Optimization Algorithm (WOA), Grasshopper Optimization Algorithm (GOA) and other algorithm variations such as binary and chaotic versions. Chapter 8 presents machine learning applications of swarm and evolutionary algorithms. Illustrative real-world examples are presented with real datasets regarding neural network optimization and feature selection, using: genetic algorithm, Geometric PSO, Chaotic Harmony Search, Chaotic Cuckoo Search, and Evolutionary Algorithm and also crime forecasting using swarm optimized SVM. In chapter 9, applications of swarm intelligence on deep long short-term memory (LSTM) networks and Deep Convolutional Neural Networks (CNNs) are discussed, including LSTM hyperparameter tuning and Covid19 diagnosis from chest X-Ray images.

The aim of the book is to present and discuss several state-of-theart swarm intelligence and evolutionary algorithms together with their variances and also several illustrative applications on Machine Learning and Deep Learning.

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