A Comprehensive Survey on Program Synthesis With Evolutionary Algorithms

• Published in: IEEE transactions on evolutionary computation Vol. 27; no. 1; pp. 82 - 97
• Main Authors: Sobania, Dominik, Schweim, Dirk, Rothlauf, Franz
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.02.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Benchmark testing; Benchmarks; Codes; Evolutionary algorithms; Evolutionary computation; Genetic algorithms; genetic programming (GP); Iterative methods; Mapping; Natural languages; program synthesis; Programming; Python; Quality assessment; Software; Software development; Synthesis; Task analysis
• ISSN: 1089-778X; 1941-0026
• DOI: 10.1109/TEVC.2022.3162324

The automatic generation of computer programs is one of the main applications with practical relevance in the field of evolutionary computation. With program synthesis techniques not only software developers could be supported in their everyday work but even users without any programming knowledge could be empowered to automate repetitive tasks and implement their own new functionality. In recent years, many novel program synthesis approaches based on evolutionary algorithms have been proposed and evaluated on common benchmark problems. Therefore, we identify and discuss in this survey the relevant evolutionary program synthesis approaches in the literature and provide an in-depth analysis of their performance. The most influential approaches we identify are stack-based, grammar-guided, as well as linear genetic programming (GP). For the stack-based approaches, we identify 37 in-scope papers, and for the grammar-guided and linear GP approaches, we identify 12 and 5 papers, respectively. Furthermore, we find that these approaches perform well on benchmark problems if there is a simple mapping from the given input to the correct output. On problems where this mapping is complex, e.g., if the problem consists of several subproblems or requires iteration/recursion for a correct solution, results tend to be worse. Consequently, for future work, we encourage researchers not only to use a program's output for assessing the quality of a solution but also the way toward a solution (e.g., correctly solved subproblems).