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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.

Information technology industries --- 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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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.

Information technology industries --- 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 --- 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

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This book was established after the 8th International Workshop on Numerical and Evolutionary Optimization (NEO), representing a collection of papers on the intersection of the two research areas covered at this workshop: numerical optimization and evolutionary search techniques. While focusing on the design of fast and reliable methods lying across these two paradigms, the resulting techniques are strongly applicable to a broad class of real-world problems, such as pattern recognition, routing, energy, lines of production, prediction, and modeling, among others. This volume is intended to serve as a useful reference for mathematicians, engineers, and computer scientists to explore current issues and solutions emerging from these mathematical and computational methods and their applications.

robust optimization --- differential evolution --- ROOT --- optimization framework --- drainage rehabilitation --- overflooding --- pipe breaking --- VCO --- CMOS differential pair --- PVT variations --- Monte Carlo analysis --- multi-objective optimization --- Pareto Tracer --- continuation --- constraint handling --- surrogate modeling --- multiobjective optimization --- evolutionary algorithms --- kriging method --- ensemble method --- adaptive algorithm --- liquid storage tanks --- base excitation --- artificial intelligence --- Multi-Gene Genetic Programming --- computational fluid dynamics --- finite volume method --- JSSP --- CMOSA --- CMOTA --- chaotic perturbation --- fixed point arithmetic --- FP16 --- pseudo random number generator --- incorporation of preferences --- multi-criteria classification --- decision-making process --- multi-objective evolutionary optimization --- outranking relationships --- decision maker profile --- profile assessment --- region of interest approximation --- optimization using preferences --- hybrid evolutionary approach --- forecasting --- Convolutional Neural Network --- LSTM --- COVID-19 --- deep learning --- trust region methods --- multiobjective descent --- derivative-free optimization --- radial basis functions --- fully linear models --- decision making process --- cognitive tasks --- recommender system --- project portfolio selection problem --- usability evaluation --- multi-objective portfolio optimization problem --- trapezoidal fuzzy numbers --- density estimators --- steady state algorithms --- protein structure prediction --- Hybrid Simulated Annealing --- Template-Based Modeling --- structural biology --- Metropolis --- optimization --- linear programming --- energy central