Evolutionary Improvement of Programs

• Published in: IEEE transactions on evolutionary computation Vol. 15; no. 4; pp. 515 - 538
• Main Authors: White, D. R., Arcuri, A., Clark, J. A.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.08.2011; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Applied sciences; Artificial intelligence; Coevolution; Compilers; Computer programs; Computer science; control theory; systems; Decision theory. Utility theory; embedded systems; Equivalence; Evolutionary; Evolutionary computation; Exact sciences and technology; execution time; Genetic programming; Mathematical analysis; Mathematical models; Memory and file management (including protection and security); Memory organisation. Data processing; multiobjective optimization; non-functional criteria; Operational research and scientific management; Operational research. Management science; Operations research; Optimization; Program processors; Programming languages; search based software engineering; Semantics; Software; Sorting; Studies; Energy consumption; search based software engineering; Compilation; Evolutionary algorithm; Multiobjective programming; Probability distribution; Seeding; multiobjective optimization; Semantics; embedded systems; Compiler optimization; Non functional requirement; Oracle; non-functional criteria; Execution time; Compiler; genetic programming; Preservation; Local optimum; Knowledge representation; Coevolution; Boarded computer; Genetic algorithm; Storage management; Software engineering
• ISSN: 1089-778X; 1941-0026
• DOI: 10.1109/TEVC.2010.2083669

Most applications of genetic programming (GP) involve the creation of an entirely new function, program or expression to solve a specific problem. In this paper, we propose a new approach that applies GP to improve existing software by optimizing its non-functional properties such as execution time, memory usage, or power consumption. In general, satisfying non-functional requirements is a difficult task and often achieved in part by optimizing compilers. However, modern compilers are in general not always able to produce semantically equivalent alternatives that optimize non-functional properties, even if such alternatives are known to exist: this is usually due to the limited local nature of such optimizations. In this paper, we discuss how best to combine and extend the existing evolutionary methods of GP, multiobjective optimization, and coevolution in order to improve existing software. Given as input the implementation of a function, we attempt to evolve a semantically equivalent version, in this case optimized to reduce execution time subject to a given probability distribution of inputs. We demonstrate that our framework is able to produce non-obvious optimizations that compilers are not yet able to generate on eight example functions. We employ a coevolved population of test cases to encourage the preservation of the function's semantics. We exploit the original program both through seeding of the population in order to focus the search, and as an oracle for testing purposes. As well as discussing the issues that arise when attempting to improve software, we employ rigorous experimental method to provide interesting and practical insights to suggest how to address these issues.