Chapter 2: Introduction to Meta-heuristic and Evolutionary Algorithms
Summary
This chapter begins with a brief review of different independent-problem methods for searching the decision space, describes the components of meta-heuristic and evolutionary algorithms by relating them to engineering optimization problems. Other related topics such as coding meta-heuristic and evolutionary algorithms, dealing with constraints, objective functions, solution strategies, are reviewed. A general algorithm is presented that encompasses most of the steps of all known meta-heuristic and evolutionary algorithms. This generic presentation provides a standard reference with which to compare all the known meta-heuristic and evolutionary algorithms. The chapter closes with the performance evaluation of the meta-heuristic and evolutionary algorithms covered by the book.
2.1 Searching decision space for optima
2.2 Definition of terms related meta-heuristic and evolutionary algorithms
2.3 Foundation of meta-heuristic and evolutionary algorithms
2.4 Classification of meta-heuristic and evolutionary algorithms
2.5 Coding meta-heuristic and evolutionary algorithms in both discrete and continuous domains
2.6 Generating random values
2.7 Dealing with constraints
2.8 Fitness functions
2.9 Selection of decision variables, parameters
2.10 Generating new solutions
2.11 The best solution
2.12 Termination criteria
2.13 General algorithm
2.14 Performance evaluation of meta-heuristic and evolutionary algorithms
2.15 Conclusion

Chapter 3: Pattern Search (PS)
Summary
This chapter explains the pattern search (PS) algorithm, which is classified as a direct search method. The chapter starts with a brief literature review of the development of PS, important modification of the algorithm, and its applications to engineering domains. The basic idea underlying the algorithm is presented and mapped to its mathematical statement. Afterwards, the different steps of the algorithm are described in detail. A pseudo code of the algorithm is presented that serves as an easy and sufficient guideline for coding the algorithm.
3.1 Introduction
3.2 Pattern search (PS) foundation
3.3 Generating initial solution
3.4 Generate trial solutions
3.5 Update mesh size
3.6 Termination criteria
3.7 User-defined parameters of the PS
3.8 Pseudo code of the PS
3.9 Conclusion
3.10 References

Chapter 4: The Genetic Algorithm (GA)
Summary
This chapter describes the genetic algorithm (GA), which is a well-known evolutionary algorithm. The chapter starts with a brief literature review of the GA's development, followed by presentation of the modification that it has experienced, and its applications in engineering domains. The basic idea underlying the algorithm is presented and mapped to its mathematical statement. Afterwards, the different steps of the algorithm are described in detail. A pseudo code of the algorithm is presented that serves as an easy and sufficient guideline for coding the algorithm.
4.1 Introduction
4.2 Mapping natural evolution into genetic algorithm (GA)
4.3 Creating the initial population
4.4 Selection of decision variables, parameters
4.4.1. Proportionate selection
4.4.2. Ranking selection
4.4.3. Tournament selection
4.5 Reproduction
4.6 Population diversity and selective pressure4.7 T