This chapter presents popular meta-heuristics inspired from nature focusing on evolutionary computation (EC). The first section, as an elevator pitch, briefly walks through problem solving, touching upon notions such as optimization problems, meta-heuristics, constraint handling, hybridization, and the No Free Lunch Theorem for optimization, and also giving very short introductions into several most popular meta-heuristics. The next two sections are dedicated to evolutionary algorithms and swarm intelligence (SI), two of the main areas of EC. Three particular optimization methods illustrating these two areas are presented in more detail: genetic algorithms (GAs), differential evolution (DE), and particle swarm optimization (PSO). For a better understanding of these algorithms, references to R packages implementing the algorithms and code samples to solve numerical and combinatorial problems are given. The fourth section is dedicated to the use of EC techniques in data analysis. Optimization of the hyper-parameters of conventional machine learning techniques is illustrated by a case study. The last section reviews applications of meta-heuristics in geosciences.
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