Genetic programming using dynamic population variation for computational efforts reduction in system modeling

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@Article{Tao:2012:JSJU,

• author = "Yan-yun Tao and Jian Cao and Ming-lu Li",
• title = "Genetic programming using dynamic population variation for computational efforts reduction in system modeling",
• journal = "Journal of Shanghai Jiaotong University (Science)",
• year = "2012",
• volume = "17",
• number = "2",
• pages = "190--196",
• keywords = "genetic algorithms, genetic programming, dynamic population variation (DPV), stagnation phase, exponential pivot function, computationaleffort, average number of evaluation, diversity",
• URL = "http://link.springer.com/article/10.1007/s12204-012-1251-7",
• DOI = "doi:10.1007/s12204-012-1251-7",
• size = "7 pages",
• abstract = "we propose genetic programming (GP) using dynamic population variation (DPV)with four innovations for reducing computational efforts. A new stagnation phase definition and characteristicmeasure are defined for our DPV. The exponential pivot function is proposed to our DPV method in conjunctionwith the new stagnation phase definition. An appropriate population variation formula is suggested to accelerateconvergence. The efficacy of these innovations in our DPV is examined using six benchmark problems. Comparisonamong the different characteristic measures has been conducted for regression problems and the new proposedmeasure outperformed other measures. It is proved that our DPV has the capacity to provide solutions at a lowercomputational effort compared with previously proposed DPV methods and standard genetic programming inmost cases. Meanwhile, our DPV approach introduced in GP could also rapidly find an excellent solution as wellas standard GP in system modeling problems.",

}

Genetic Programming entries for Yanyun Tao Jian Cao Minglu Li

Citations