A new model for evolving Evolutionary Algorithms is proposed in this paper. The model is based on the Linear Genetic Programming (LGP) technique. Every LGP chromosome encodes an EA which is used for solving a particular problem. Several Evolutionary Algorithms for function optimization, the Traveling Salesman Problem, and the Quadratic Assignment Problem are evolved by using the considered model. Numerical experiments show that the evolved Evolutionary Algorithms perform similarly and sometimes even better than standard approaches for several well-known benchmarking problems.

翻译：本文件提出了演化进化算法的新模式,该模式以线性遗传学方案(LGP)技术为基础。每个LGP染色体都编码了用于解决特定问题的EA。功能优化的几种进化性算法、旅行推销员问题和二次曲线分配问题都是通过使用经过考虑的模型来演变的。数字实验表明,进化进化的进化算法在几个众所周知的基准问题上的表现类似,有时甚至比标准方法要好。