Abstract
This chapter introduces the basic evolutionary algorithms, including the canonical genetic algorithms, real-coded genetic algorithms, evolution strategies, genetic programming, ant colony optimization algorithms, particle swarm optimization, and differential evolution. In addition, memetic algorithms that combine evolutionary search with local search, and estimation of distribution algorithms that use a probabilistic model to generate offspring solutions will also be described. Finally, basic methodologies for solving multi- and many-objective optimization problems are introduced.