Self-adaptive genetically programmed differential evolution

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@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