When dealing with real-world applications, one often faces non-linear and nondifferentiable optimization problems which do not allow the employment of exact methods. In addition, as highlighted in [104], popular local search methods (e.g. Hooke-Jeeves, Nelder Mead and Rosenbrock) can be ill-suited when the real-world problem is characterized by a complex and highly multi-modal fitness landscape since they tend to converge to local optima. In these situations, population based meta-heuristics can be a reasonable choice, since they have a good potential in detecting high quality solutions. For these reasons, meta-heuristics, such as Genetic Algorithms (GAs), Evolution Strategy (ES), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Differential Evolution (DE), have been extensively applied in engineering and design problems. © 2012 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Caponio, A., & Neri, F. (2012). Memetic algorithms in engineering and design. Studies in Computational Intelligence, 379, 241–260. https://doi.org/10.1007/978-3-642-23247-3_15
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