Abstract
In this chapter, the authors explain basic concepts related to the fundamentals of genetic algorithms. Initially, they discuss the progress of and interest in GAs. Then, they describe the general structure and formulation of a GA, including essential aspects such as coding and fitness calculation for mono-objective optimization (MoOO) and multiobjective optimization (MuOO) problems. The authors explain classical ways to implement the selection, crossover, and mutation operators using binary, integer, and real coding, and discuss basic ideas for the implementation of elitist and nonelitist approaches for MoOO and MuOO. In the case of MuOO, they describe several classical algorithms such as multiobjective genetic algorithm, nondominated sorting genetic algorithm, elitist nondominated sorting genetic algorithm, strength Pareto evolutionary algorithm (SPEA), and its improved version, SPEA2. Thus, this chapter offers a broad perspective on the implementation and rudiments of a GA.
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