Genetic improvement: A key challenge for evolutionary computation

Citation

Langdon WB & Ochoa G (2016) Genetic improvement: A key challenge for evolutionary computation. In: 2016 IEEE Congress on Evolutionary Computation, CEC 2016. 2016 IEEE Congress on Evolutionary Computation (CEC), Vancouver, Canada, 24.07.2016-29.07.2016. Piscataway, NJ, USA: IEEE, pp. 3068-3075. https://doi.org/10.1109/CEC.2016.7744177

Abstract
Automatic Programming has long been a sub-goal of Artificial Intelligence (AI). It is feasible in limited domains. Genetic Improvement (GI) has expanded these dramatically to more than 100 000 lines of code by building on human written applications. Further scaling may need key advances in both Search Based Software Engineering (SBSE) and Evolutionary Computation (EC) research, particularly on representations, genetic operations, fitness landscapes, fitness surrogates, multi objective search and co-evolution.

Keywords
Genetics; Computer bugs; Software engineering; Sociology; Statistics; Evolutionary computation; Artificial intelligence