@techreport{TR-IC-PFG-18-13,
   number = {IC-PFG-18-13},
   author  = {Guilherme Bueno {Andrade} and Andre Rodrigues {Oliveira}
                   and Zanoni {Dias}},
   title  =  {{Sorting  Permutations  by  Reversals with Reinforcement
                   Learning}},
   month = {July},
   year = {2018},
   institution = {Institute of Computing, University of Campinas},
   note = {In English, 11 pages.
    \par\selectlanguage{english}\textbf{Abstract}
       Finding  the  minimum  number  of  mutations  necessary for one
       genome  to  transform  into  another  is  a  major  problem  in 
       molecular  biology.  If  genomes  are  represented  as  numeric 
       permutations,  this  problem  can  be  reduced  to sorting such
       permutations  using  certain  genome rearrangements operations,
       where,  in  this work, reversals operations are the main focus.
       We   present   two  different  techniques  using  reinforcement 
       learning  to  address that. Our results show that this approach
       is  competitive  for permutations of size $n < 11$. However, as
       the permutations grow, converging gets trickier.
  }
}