Summary2.1 Searching the Decision Space for Optimal Solutions2.2 Definition of Terms of Meta‐Heuristic and Evolutionary Algorithms2.3 Principles of Meta‐Heuristic and Evolutionary Algorithms2.4 Classification of Meta‐Heuristic and Evolutionary Algorithms2.5 Meta‐Heuristic and Evolutionary Algorithms in Discrete or Continuous Domains2.6 Generating Random Values of the Decision Variables2.7 Dealing with Constraints2.8 Fitness Function2.9 Selection of Solutions in Each Iteration2.10 Generating New Solutions2.11 The Best Solution in Each Algorithmic Iteration2.12 Termination Criteria2.13 General Algorithm2.14 Performance Evaluation of Meta‐Heuristic and Evolutionary Algorithms2.15 Search Strategies2.16 ConclusionReferences