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This book was established after the 6th 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.

model predictive control --- bulbous bow --- improvement differential evolution algorithm --- evolutionary multi-objective optimization --- location routing problem --- flexible job shop scheduling problem --- basic differential evolution algorithm --- metric measure spaces --- NEAT --- genetic algorithm --- multiobjective optimization --- improved differential evolution algorithm --- performance indicator --- rubber --- averaged Hausdorff distance --- mixture experiments --- U-shaped assembly line balancing --- Genetic Programming --- Local Search --- driving events --- surrogate-based optimization --- single component constraints --- crop planning --- Pareto front --- numerical simulations --- shape morphing --- genetic programming --- economic crops --- local search and jump search --- model order reduction --- optimal solutions --- EvoSpace --- risky driving --- intelligent transportation systems --- optimal control --- IV-optimality criterion --- Bloat --- decision space diversity --- modify differential evolution algorithm --- power means --- driving scoring functions --- open-source framework --- evolutionary computation --- differential evolution algorithm --- vehicle routing problem --- multi-objective optimization