Evolving a Nelder-Mead Algorithm for Optimization with Genetic Programming

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@Article{Fajfar:2016:EC,

• author = "Iztok Fajfar and Janez Puhan and Arpad Burmen",
• title = "Evolving a Nelder-Mead Algorithm for Optimization with Genetic Programming",
• journal = "Evolutionary Computation",
• year = "2017",
• volume = "25",
• number = "3",
• pages = "351--373",
• month = "Fall",
• keywords = "genetic algorithms, genetic programming",
• ISSN = "1063-6560",
• DOI = "doi:10.1162/EVCO_a_00174",
• size = "23 page",
• abstract = "We use genetic programming to evolve a direct search optimization algorithm, similar to that of the standard downhill simplex optimization method proposed by Nelder and Mead (1965). In training process, we use several 10-dimensional quadratic functions with randomly displaced parameters and different randomly generated starting simplices. The genetically obtained optimization algorithm shows overall better performance than the original Nelder-Mead method on a standard set of test functions. We observe that many parts of the genetically produced algorithm are seldom or never executed, which allows us to greatly simplify the algorithm by removing the redundant parts. The resulting algorithm turns out to be considerably simpler than the original Nelder-Mead method while still performing better than the original method.",

}

Genetic Programming entries for Iztok Fajfar Janez Puhan Arpad Burmen

Citations