Continuous Hierarchical Fair Competition Model for Sustainable Innovation in Genetic Programming
Continuous Hierarchical Fair Competition Model for Sustainable Innovation in Genetic Programming
复制标题
持续分层公平竞争模式促进基因编程可持续创新
DOI：
10.1007/978-1-4419-8983-3_6
复制
发表时间：
2003
期刊：
影响因子：
--
通讯作者：
Kisung Seo
中科院分区：
文献类型：
--
作者：
Jianjun Hu;E. Goodman;Kisung Seo
遗传规划算法缺乏可持续的搜索能力，严重制约了其在更复杂问题上的应用。为提高单种群遗传规划的持续创新能力，提出了一种新的进化算法模型--连续分层公平竞争模型。它是通过提取最初在多种群HFC模型中提出的可持续生物和社会过程的基本原则来设计的。在整个适应度范围内的分层精英、繁殖概率分布和个体分布控制使CHFC在实现可持续进化的同时，灵活地控制进化搜索过程。实验结果表明，该算法能够进行稳健的可持续搜索，避免了遗传规划中常见的老化问题。
Lack of sustainable search capability of genetic programming has severely constrained its application to more complex problems. A new evolutionary algorithm model named the continuous hierarchical fair competition (CHFC) model is proposed to improve the capability of sustainable innovation for single population genetic programming. It is devised by extracting the fundamental principles underlying sustainable biological and societal processes originally proposed in the multi-population HFC model. The hierarchical elitism, breeding probability distribution and individual distribution control over the whole fitness range enable CHFC to achieve sustainable evolution while enjoying flexible control of an evolutionary search process. Experimental results demonstrate its capability to do robust sustainable search and avoid the aging problem typical in genetic programming.