Random Lines: A Novel Population Set-based Evolutionary Global Optimization Algorithm

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Sahin:2011:EuroGP,
  author =       "Ismet Sahin",
  title =        "Random Lines: A Novel Population Set-based
                 Evolutionary Global Optimization Algorithm",
  booktitle =    "Proceedings of the 14th European Conference on Genetic
                 Programming, EuroGP 2011",
  year =         "2011",
  month =        "27-29 " # apr,
  editor =       "Sara Silva and James A. Foster and Miguel Nicolau and 
                 Mario Giacobini and Penousal Machado",
  series =       "LNCS",
  volume =       "6621",
  publisher =    "Springer Verlag",
  address =      "Turin, Italy",
  pages =        "97--108",
  organisation = "EvoStar",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-642-20406-7",
  DOI =          "doi:10.1007/978-3-642-20407-4_9",
  abstract =     "In this paper, we present a new population set-based
                 evolutionary optimisation algorithm which aims to find
                 global minima of cost functions. This algorithm creates
                 random lines passing through pairs of points (vectors)
                 in population, fits a quadratic function based on three
                 points on each line, and then applies the crossover
                 operation to extrema of these quadratic functions, and
                 lastly performs the selection operation. We refer to
                 the points determining random lines as parent points
                 and the extremum of a quadratic model as the descendant
                 or mutated point under some conditions. In the
                 crossover operation, some entries of a descendant
                 vector are randomly replaced with the corresponding
                 entries of one parent vector and some other entries of
                 the descendant vector are replaced with the
                 corresponding entries of the other parent vector based
                 on the crossover constant. The above crossover and
                 mutation operations make this algorithm robust and fast
                 converging. One important property of this algorithm is
                 that its robustness in general increases with
                 increasing population size which may become useful when
                 more processing units are available. This algorithm
                 achieves comparable results with the well-known
                 Differential Evolution (DE) algorithm over a wide range
                 of cost functions.",
  notes =        "Part of \cite{Silva:2011:GP} EuroGP'2011 held in
                 conjunction with EvoCOP2011 EvoBIO2011 and
                 EvoApplications2011",
}

Genetic Programming entries for Ismet Sahin

Citations