A Developmental Genetic Approach to the cost/time trade-off in Resource Constrained Project Scheduling

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

@InProceedings{Pawinski:2014:FedCSIS,
  author =       "Grzegorz Pawinski and Krzysztof Sapiecha",
  booktitle =    "Federated Conference on Computer Science and
                 Information Systems (FedCSIS 2014)",
  title =        "A Developmental Genetic Approach to the cost/time
                 trade-off in Resource Constrained Project Scheduling",
  year =         "2014",
  month =        sep,
  pages =        "171--179",
  abstract =     "In this paper, the use of Developmental Genetic
                 Programming (DGP) for solving a new extension of the
                 Resource-Constrained Project Scheduling Problem (RCPSP)
                 is investigated. We consider a variant of the problem
                 when resources are only partially available and a
                 deadline is given but it is the cost of the project
                 that should be minimised. RCPSP is a well-known NP-hard
                 problem but in its original formulation it does not
                 take into consideration initial resource workload and
                 it minimises the makespan. Unlike other genetic
                 approaches, where genotypes represent solutions, a
                 genotype in DGP is a procedure that constructs a
                 solution to the problem. Genotypes (the search space)
                 and phenotypes (the solution space) are distinguished
                 and a genotype-to-phenotype mapping (GPM) is used.
                 Thus, genotypes are evolved without any restrictions
                 and the whole search space is explored. The goal of the
                 evolution is to find a procedure constructing the best
                 solution of the problem for which the cost of the
                 project is minimal. The paper presents genetic
                 operators as well as GPM specified for the DGP.
                 Experimental results showed that our approach gives
                 significantly better results compared with methods
                 presented in the literature.",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.15439/2014F151",
  notes =        "ACSIS vol 2. Also known as \cite{6933010}",
}

Genetic Programming entries for Grzegorz Pawinski Krzysztof Sapiecha

Citations