Self-adaptive genetically programmed differential evolution

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

@InProceedings{Roy:2012:ICECE,
  author =       "Pravakar Roy and Md. Jahidul Islam and 
                 Md. Monirul Islam",
  booktitle =    "7th International Conference on Electrical Computer
                 Engineering (ICECE 2012)",
  title =        "Self-adaptive genetically programmed differential
                 evolution",
  year =         "2012",
  pages =        "639--642",
  address =      "Dhaka, Bangladesh",
  month =        "20-22 " # dec,
  isbn13 =       "978-1-4673-1434-3",
  DOI =          "doi:10.1109/ICECE.2012.6471631",
  abstract =     "Differential evolution (DE) is a simple and efficient
                 technique for real parameter optimisation over
                 continuous spaces. Its success is highly dependent on
                 the choice of correct trial vector generation
                 strategies and control parameters. Choosing appropriate
                 trial vector generation strategies and control
                 parameters for new problems by trial and error method
                 can be computationally costly and inefficient. This
                 paper proposes a hybrid approach, incorporating genetic
                 programming (GP) with DE, where GP generates trial
                 vector generation strategies based on the problem
                 specification and the state of the population using a
                 simple learning method. Trial vector generation
                 strategies are chosen from this pool of strategies
                 generated by GP. The choice of a particular strategy
                 depends on the type of the problem, initialisation
                 values and state of evolution. Consequently, the
                 strategies chosen for different run of the same problem
                 are different. However, it allows self-adaptation to be
                 completely problem dependent and as a result for a
                 unknown problem domain the method is expected to
                 perform better than other state-of-the-art
                 self-adaptive evolutionary techniques. In this method,
                 the control parameter F is eliminated and crossover
                 ratio Cr is evolved with the population and population
                 size NP is still fixed empirically. The performance of
                 this method is extensively evaluated using the CEC2005
                 contest test instances. Experimental results show that,
                 self-adaptive genetically programmed differential
                 evolution (SaGPDE) leads to quick convergence and
                 produce very competitive results.",
  keywords =     "genetic algorithms, genetic programming, differential
                 equations, learning (artificial intelligence), CEC2005
                 contest test instances, SaGPDE, continuous spaces,
                 control parameters, real parameter optimisation,
                 self-adaptive evolutionary techniques, self-adaptive
                 genetically programmed differential evolution, simple
                 learning method, trial and error method, trial vector
                 generation strategies, Erbium, Evolutionary
                 computation, Optimisation, Sociology, Statistics,
                 Vectors, Differential evolution, self-adaptation, trial
                 vector generation strategy",
  notes =        "Also known as \cite{6471631}",
}

Genetic Programming entries for Pravakar Roy Md Jahidul Islam Md Monirul Islam

Citations