@Bibtex-file{Ai/memetic.bib,
  title =        "Bibliography on Memetic algorithms",
  author =       "Pablo Moscato",
  email =        "moscato@densis.fee.unicamp.br",
  address =      "DENSIS - FEEC - UNICAMP (Universidade Estadual de
                 Campinas) \\ Brazil",
  supported =    "yes",
  abstract =     "Memetic Algorithms is a population-based approach for
                 heuristic search in optimization problems. They have
                 shown that they are orders of magnitude faster than
                 traditional Genetic Algorithms for some problem
                 domains. Basically, they combine local search
                 heuristics with crossover operators. For this reason,
                 some researchers have viewed them as Hybrid Genetic
                 Algorithms. However, combinations with constructive
                 heuristics or exact methods may also belong to this
                 class of metaheuristics. Since they are most suitable
                 for MIMD parallel computers and distributed computing
                 systems (including heterogeneous systems) as those
                 composed by networks of workstations, they have also
                 received the dubious denomination of Parallel Genetic
                 Algorithms. Other researchers known it as Genetic Local
                 Search.",
}