@Article{         renault.20.isse,
  author        = {Etienne Renault},
  title         = {Improving swarming using genetic algorithms},
  journal       = {Innovations in Systems and Software Engineering: a NASA
                  journal (ISSE)},
  year          = 2020,
  volume        = {16},
  number        = {2},
  pages         = {143--159},
  month         = jun,
  publisher     = {Springer},
  abstract      = { The verification of temporal properties against a given
                  system may require the exploration of its full state space.
                  In explicit model checking, this exploration uses a
                  depth-first search and can be achieved with multiple
                  randomized threads to increase performance. Nonetheless,
                  the topology of the state space and the exploration order
                  can cap the speedup up to a certain number of threads. This
                  paper proposes a new technique that aims to tackle this
                  limitation by generating artificial initial states, using
                  genetic algorithms. Threads are then launched from these
                  states and thus explore different parts of the state space.
                  Our prototype implementation is 10\% faster than
                  state-of-the-art algorithms on a general benchmark and 40\%
                  on a specialized benchmark. Even if we expected a decrease
                  in an order of magnitude, these results are still
                  encouraging since they suggest a new way to handle existing
                  limitations. Empirically, our technique seems well suited
                  for "linear topology", i.e., the one we can obtain when
                  combining model checking algorithms with partial-order
                  reduction techniques. },
  doi           = {10.1007/s11334-020-00362-7}
}