A specialized thread of metaheuristic research, bordering and often overlapping with Artificial Intelligence, studied heuristics that evolved whole sets of candidate solutions, often named “populations” of solutions. Genetic algorithms were among the first results, and following their success it became common to get inspiration from some natural phenomenon to design the heuristic. This chapter considers three representative population-evolving metaheuristics, namely genetic algorithms, ant colony optimization, and scatter search (with path relinking) and shows how they have been complemented with mathematical programming modules to achieve better performance.
Maniezzo, V., Boschetti, M.A., Stützle, T. (2021). Population-Based Metaheuristics. Cham : Springer [10.1007/978-3-030-70277-9_4].
Population-Based Metaheuristics
Maniezzo, Vittorio;Boschetti, Marco Antonio;
2021
Abstract
A specialized thread of metaheuristic research, bordering and often overlapping with Artificial Intelligence, studied heuristics that evolved whole sets of candidate solutions, often named “populations” of solutions. Genetic algorithms were among the first results, and following their success it became common to get inspiration from some natural phenomenon to design the heuristic. This chapter considers three representative population-evolving metaheuristics, namely genetic algorithms, ant colony optimization, and scatter search (with path relinking) and shows how they have been complemented with mathematical programming modules to achieve better performance.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
