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. | Swarm Intelligence and Evolutionary Computation Theory Advances and Applications in Machine Learning and Deep Learning
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. | Swarm Intelligence and Evolutionary Computation Theory Advances and Applications in Machine Learning and Deep Learning
Sellers offer a range of delivery options, so you can choose the one that’s most convenient for you. Many sellers offer free delivery. You can always find the postage cost and estimated delivery date in a seller’s listing. You'll then be able to see a full list of delivery options during checkout. These can include: Express delivery, Standard delivery, Economy delivery, Click & Collect, Free local collection from seller.
Your options for returning an item vary depending on what you want to return, why you want to return it, and the seller's return policy. If the item is damaged or doesn't match the listing description, you can return it even if the seller's returns policy says they don't accept returns. If you've changed your mind and no longer want an item, you can still request a return, but the seller doesn't have to accept it. If the buyer changes their mind about a purchase and wants to return an item, they may need to pay return postage costs, depending on the seller's return policy. Sellers can provide a return postage address and additional return postage information for the buyer. Sellers pay for return postage if there's a problem with the item. For example, if the item doesn't match the listing description, is damaged or defective or is counterfeit. By law, customers in the European Union also have the right to cancel the purchase of an item within 14 days beginning from the day you receive, or a third party indicated by you (other than the carrier) receives, the last good ordered by you (if delivered separately). This applies to all products except for digital items (e.g. Digital Music) that are provided immediately to you with your acknowledgement, and other items such as video, DVD, audio, video games, Sex and Sensuality products and software products where the item has been unsealed.
Sellers have to offer a refund for certain items only if they are faulty, such as: Personalised items and custom-made items, Perishable items, Newspapers and magazines, Unwrapped CDs DVDs and computer software. If you used your PayPal balance or bank account to fund the original payment, the refunded money will go back to your PayPal account balance. If you used a credit or debit card to fund the original payment, the refunded money will go back to your card. The seller will effect the refund within three working days but it may take up to 30 days for Paypal to process the transfer. For payments funded partially by a card and partially by your balance/bank, the money taken from your card will go back to your card and the remainder will return to your PayPal balance.