The search performance of conventional Genetic Programming (GP) methods is strongly guided by the performance of the fitness function. In each generation, the fitness function evaluates every program in the population and measures the distance between the final output of the programs and the desired output. Human programmers often rely on the feedback from the intermediate execution states, which are the semantics, to localize and resolve software bugs. However, the semantics of a program is seldom explicitly considered in the fitness function to assess the quality of a program in GP. In this paper, we invent methods to improve fitness evaluation leveraging semantics in GP. We propose semantics flow analysis for programs using information theoretic concepts. Next, we develop a novel semantic fitness evaluation technique to rank programs using semantics based on the semantics flow analysis. Our evaluation results show that adopting our method can improve the success rates in Grammar-Based GP.
|Title of host publication||GECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion|
|Publisher||Association for Computing Machinery (ACM)|
|Number of pages||2|
|Publication status||Published - 13 Jul 2019|
|Event||2019 Genetic and Evolutionary Computation Conference, GECCO 2019 - Prague, Czech Republic|
Duration: 13 Jul 2019 → 17 Jul 2019
|Name||GECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion|
|Conference||2019 Genetic and Evolutionary Computation Conference, GECCO 2019|
|Abbreviated title||GECCO 19|
|Period||13/07/19 → 17/07/19|
Bibliographical noteThis research is supported by Institute of Future Cities of The Chinese University of Hong Kong.
- Genetic Programming
- Semantics Flow