【摘要】 分析了基于拉马克主义的进化学习策略(简称LELS)和基于达尔文主义的进化学习策略(简称DELS)的异同;前者的生物学依据是后天获得性遗传,而后者的依据是自然选择;前者的表现型和基因型在学习的过程中同时被优化;而后者表现型的变化不会直接导致基因型的改变.利用马尔可夫链理论证明了此类算法的收敛性,并且在理论上分析了DELS具有更强的局部逃逸能力.在仿真试验中应用8个标准测试函数进行测试,结果表明此类算法具有较好的全局优化能力和较快的收敛速度,其中DELS的逃逸能力更强.更多还原

【Abstract】 The Lamarckian evolution learning strategy(LELS)and Darwinian evolution learning strategy(DELS)are discussed in terms of their similarities and differences in learn- ing implementation.The former is based on inheritance of acquired character,i.e.,both phenotype and genotype can be optimized through learning while the later only optimizes phenotype based on Darwinian selection.The convergence of ELS is proved using Markov chain theory.And we also theoretically demonstrate that DELS has stronger escaping capac- ity.These algorithms are applied to 8 standard test functions.Simulation results show that LELS and DELS yield faster convergence and better global optimization ability than stan- dard evolution strategies;moreover,DELS also leads to better escaping capacity.Finally, limitations of the work as well as the future study are discussed.更多还原