Mahmoud A. Bokhari
Markus Wagner
ABSTRACT
Many challenges arise in the application of Genetic Improvement (GI) of Software to improve non-functional requirements of software such as energy use and run-time. These challenges are mainly centred around the complexity of the search space and the estimation of the desired fitness function. For example, such fitness function are expensive, noisy and estimating them is not a straightforward task. In this paper, we illustrate some of the challenges in computing such fitness functions and propose a synergy between in-vivo evaluation and machine learning approaches to overcome such issues.
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Index Terms
- Genetic improvement of software efficiency: the curse of fitness estimation
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