Probabilistic Incremental Program Evolution

作     者：Salustowicz, Rafal Schmidhuber, Juergen 

作者机构：IDSIA CH-6900 Lugano Switzerland 

出 版 物：《EVOLUTIONARY COMPUTATION》 (调优计算)

年 卷 期：1997年第5卷第2期

页      面：123-141页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Probabilistic incremental program evolution probabilistic programming languages stochastic program search population-based incremental learning genetic programming partially observable environments 

摘      要：Probabilistic incremental program evolution (PIPE) is a novel technique for automatic program synthesis. We combine probability vector coding of program instructions, population-based incremental learning, and tree-coded programs like those used in some variants of genetic programming (GP). PIPE iteratively generates successive populations of functional programs according to an adaptive probability distribution over all possible programs. Each iteration, it uses the best program to refine the distribution. Thus, it stochastically generates better and better programs. Since distribution refinements depend only on the best program of the current population, PIPE can evaluate program populations efficiently when the goal is to discover a program with minimal runtime. We compare PIPE to GP on a function regression problem and the 6-bit parity problem. We also use PIPE to solve tasks in partially observable mazes, where the best programs have minimal runtime.