In this paper,we propose genetic programming(GP) using dynamic population variation(DPV) with four innovations for reducing computational efforts.A new stagnation phase definition and characteristic measure are define...In this paper,we propose genetic programming(GP) using dynamic population variation(DPV) with four innovations for reducing computational efforts.A new stagnation phase definition and characteristic measure are defined for our DPV.The exponential pivot function is proposed to our DPV method in conjunction with the new stagnation phase definition.An appropriate population variation formula is suggested to accelerate convergence.The efficacy of these innovations in our DPV is examined using six benchmark problems.Comparison among the difierent characteristic measures has been conducted for regression problems and the new proposed measure outperformed other measures.It is proved that our DPV has the capacity to provide solutions at a lower computational effort compared with previously proposed DPV methods and standard genetic programming in most cases.Meanwhile,our DPV approach introduced in GP could also rapidly find an excellent solution as well as standard GP in system modeling problems.展开更多
In the actuarial literature, several exact and approximative recursive methods have been proposed for calculating the distribution of a sum of mutually independent compound Bernoulli distributed random variables. In t...In the actuarial literature, several exact and approximative recursive methods have been proposed for calculating the distribution of a sum of mutually independent compound Bernoulli distributed random variables. In this paper, we give an overview of these methods. We compare their performance with the straight- forward convolution technique by counting the number of dot operations involved in each method. It turns out that in many practicle situations, the recursive methods outperform the convolution method.展开更多
基金Ministry of Major Science & Technology of Shanghai(No.10DZ1200204)
文摘In this paper,we propose genetic programming(GP) using dynamic population variation(DPV) with four innovations for reducing computational efforts.A new stagnation phase definition and characteristic measure are defined for our DPV.The exponential pivot function is proposed to our DPV method in conjunction with the new stagnation phase definition.An appropriate population variation formula is suggested to accelerate convergence.The efficacy of these innovations in our DPV is examined using six benchmark problems.Comparison among the difierent characteristic measures has been conducted for regression problems and the new proposed measure outperformed other measures.It is proved that our DPV has the capacity to provide solutions at a lower computational effort compared with previously proposed DPV methods and standard genetic programming in most cases.Meanwhile,our DPV approach introduced in GP could also rapidly find an excellent solution as well as standard GP in system modeling problems.
基金Support by the Onderzoeksfonds K.U.Leuven(GOA/02:Actuarile,financile en statistische aspecten van afhankelijkheden in vcrzekerings-en financile portefeuilles)Support by the Dutch Organization for Scientific Research(No.NWO 048.031.2003.001)
文摘In the actuarial literature, several exact and approximative recursive methods have been proposed for calculating the distribution of a sum of mutually independent compound Bernoulli distributed random variables. In this paper, we give an overview of these methods. We compare their performance with the straight- forward convolution technique by counting the number of dot operations involved in each method. It turns out that in many practicle situations, the recursive methods outperform the convolution method.
