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A New Dynamic Population Variation in Genetic Programming

Yanyun Tao, Minglu Li, Jian Cao


A dynamic population variation (DPV) in genetic programming (GP) with four innovations is proposed for reducing computational effort and accelerating convergence during the run of GP. Firstly, we give a new stagnation phase definition and the characteristic measure for it. Secondly, we propose an exponential pivot function (EXP) in conjunction with the new stagnation phase definition. Thirdly, we propose an appropriate population variation formula for EXP. Finally, we introduce a scheme using an instruction matrix for producing new individuals to maintain diversity of the population. The efficacy of these innovations in our DPV is examined using four typical benchmark problems. Comparisons among the different characteristic measures have been conducted for regression problems and the proposed measure performed best in all characteristic measures. It is demonstrated that the proposed population variation scheme is superior to fixed and proportionate population variation schemes for sequence induction. It is proved that the new DPV has the capacity to provide solutions at a lower computational effort compared with previously proposed population variation methods and standard genetic programming in most problems.

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