Despite recent increase in research on improvement of non-functional properties of
software, such as energy usage or program size, there is a lack of standard benchmarks for …

Abstract Information theory and entropy loss predict deeper more hierarchical software will
be more robust. Suggesting silent errors and equivalent mutations will be more common in …

Genetic improvement (GI) uses automated search to improve existing software. It has been
successfully used to optimize various program properties, such as runtime or energy …

GPUs are a key enabler of the revolution in machine learning and high-performance
computing, functioning as de facto co-processors to accelerate large-scale computation. As …

Using MAGPIE (Machine Automated General Performance Improvement via Evolution of
software), we measure the impact of genetic improvement (GI) on a non-deterministic deeply …

In the autonomic computing context, the system is perceived as a set of autonomous
elements capable of self-management, where end-users define high-level goals and the …

Genetic improvement (GI) improves both functional properties of software, such as bug
repair, and non-functional properties, such as execution time, energy consumption, or …

In modern datacenters, huge energy consumption is a significant problem that remains to be
solved. Previous works reduce the system energy consumption by switching the idle servers …

GenProg implemented a novel method for automatically evolving patches to repair test suite
failures in legacy C programs. It combined insights from genetic programming and software …

As the programming stack and tool support for GPU have matured, GPUs have become
accessible to programmers who often lack domain-specific knowledge of the underlying …