摘要
根据卡尔.波普尔的知识进化论原理,建立了知识进化策略的基本框架,并给出了一种用于求解无约束非线性优化问题的具体实现步骤。知识进化策略的核心思想,就是假说集与知识集的协同进化,二者之间通过猜测与反驳法联系起来,其进化结果最终逼近真理,即待求解问题的最优解。对12个经典测试函数进行性能仿真实验,结果表明该算法收敛速度有很大提高,并且在一定程度上抑制了早熟现象。
Based on evolutionary epistemology proposed by Sir Karl Raimund Popper, a computational framework of Knowledge Evolution Strategies （KES） is proposed at first. The key idea behind Knowledge Evolution Strategies is that both of hypotheses set （H） and knowledge set （K）, which are connected with conjecture and refutation method （CRM）, evolve into the truth cooperatively, i.e. the best solutions of the problem to be solved. Then a specific implementation of Knowledge Evolution Strategies for nonlinear unconstraint optimiza tion problems is produced. And 12 nonlinear multidimensional unconstraint function optimization problems are investigated. The computational experiment shows that Knowledge Evolution Strategies can produce substantial performance improvements as expressed in terms of high speed for convergence and avoid the premature convergence to some degree.
出处
《系统工程与电子技术》
EI
CSCD
北大核心
2007年第6期1017-1020,F0003,共5页
Systems Engineering and Electronics
关键词
进化计算
知识进化策略
知识进化论
假说集
知识集
猜测与反驳法
evolutionary computation
knowledge evolution strategies
evolutionary epistemology
hypotheses set
knowledge set
conjecture and refutation method
