Search Based Software Engineering techniques are emerging as important tools for software maintenance. Foremost among these is Genetic Improvement, which has historically applied the stochastic techniques of Genetic Programming to optimize pre-existing program code. Previous work in this area has not generally preserved program semantics and this article describes an alternative to the traditional mutation operators used, employing deterministic proof search in the sequent calculus to yield semantics-preserving transformations on algebraic data types. Two case studies are described, both of which are applicable to the recently-introduced ‘grow and graft’ technique of Genetic Improvement: the first extends the expressiveness of the ‘grafting’ phase and the second transforms the representation of a list data type to yield an asymptotic efficiency improvement.

中文翻译：

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遗传编程 $$\varvec{+}$$ + 证明搜索 $$\varvec{=}$$ = 自动改进

基于搜索的软件工程技术正在成为软件维护的重要工具。其中最重要的是遗传改进，它历来应用遗传编程的随机技术来优化预先存在的程序代码。该领域以前的工作通常没有保留程序语义，本文描述了所使用的传统变异算子的替代方法，在后续演算中采用确定性证明搜索来产生对代数数据类型的语义保留转换。描述了两个案例研究，这两个案例研究都适用于最近引入的“生长和移植”遗传改良技术：