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Evolutionary algorithms and metaheuristics are widely used to provide efficient and effective approximate solutions to computationally hard optimization problems. With the widespread use of intelligent systems in recent years, evolutionary algorithms have been applied, beyond classical optimization problems, to AI system parameter optimization and the design of artificial neural networks and feature selection in machine learning systems. This volume will present recent results of applications of the most successful metaheuristics, from differential evolution and particle swarm optimization to artificial neural networks, loT allocation, and multi-objective optimization problems. It will also provide a broad view of the role and the potential of evolutionary algorithms as service components in Al systems.

multi-objective optimization problems --- particle swarm optimization (PSO) --- Gaussian mutation --- improved learning strategy --- big data --- interval concept lattice --- horizontal union --- sequence traversal --- evolutionary algorithms --- multi-objective optimization --- parameter puning --- parameter analysis --- particle swarm optimization --- differential evolution --- global continuous optimization --- wireless sensor networks --- task allocation --- stochastic optimization --- social network optimization --- memetic particle swarm optimization --- adaptive local search operator --- co-evolution --- PSO --- formal methods in evolutionary algorithms --- self-adaptive differential evolutionary algorithms --- constrained optimization --- ensemble of constraint handling techniques --- hybrid algorithms --- association rules --- mining algorithm --- vertical union --- neuroevolution --- neural networks --- n/a

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Energy has been a crucial element for human beings and sustainable development. The issues of global warming and non-green energy have yet to be resolved. This book is a collection of twelve articles that provide strong evidence for the success of artificial intelligence deployment in energy research, particularly research devoted to non-intrusive load monitoring, network, and grid, as well as other emerging topics. The presented artificial intelligence algorithms may provide insight into how to apply similar approaches, subject to fine-tuning and customization, to other unexplored energy research. The ultimate goal is to fully apply artificial intelligence to the energy sector. This book may serve as a guide for professionals, researchers, and data scientists—namely, how to share opinions and exchange ideas so as to facilitate a better fusion of energy, academic, and industry research, and improve in the quality of people's daily life activities.

artificial neural network --- home energy management systems --- conditional random fields --- LR --- ELR --- energy disaggregation --- artificial intelligence --- genetic algorithm --- decision tree --- static young’s modulus --- price --- scheduling --- self-adaptive differential evolution algorithm --- Marsh funnel --- energy --- yield point --- non-intrusive load monitoring --- mud rheology --- distributed genetic algorithm --- MCP39F511 --- Jetson TX2 --- sustainable development --- artificial neural networks --- transient signature --- load disaggregation --- smart villages --- ambient assisted living --- smart cities --- demand side management --- smart city --- CNN --- wireless sensor networks --- object detection --- drill-in fluid --- ERELM --- sandstone reservoirs --- RPN --- deep learning --- RELM --- smart grids --- multiple kernel learning --- load --- feature extraction --- NILM --- energy management --- energy efficient coverage --- insulator --- Faster R-CNN --- home energy management --- smart grid --- LSTM --- smart metering --- optimization algorithms --- forecasting --- plastic viscosity --- machine learning --- computational intelligence --- policy making --- support vector machine --- internet of things --- sensor network --- nonintrusive load monitoring --- demand response