Tuning compilations is the process of adjusting the values of a compiler options to improve some features of the final application. In this paper, a strategy based on the use of a genetic algorithm and a multi-objective scheme is proposed to deal with this task. Unlike previous works, we try to take advantage of the knowledge of this domain to provide a problem-specific genetic operation that improves both the speed of convergence and the quality of the results. The evaluation of the strategy is carried out by means of a case of study aimed to improve the performance of the well-known web server Apache. Experimental results show that a 7.5% of overall improvement can be achieved. Furthermore, the adaptive approach has shown an ability to ma...
International audience—Elasticity [19] is a key feature for cloud infrastruc-tures to continuously a...
The Optimization Selection Problem is widely known in computer science for its complexity and import...
A way to reduce the cost of regression testing consists of selecting or prioritizing subsets of test...
Modern compilers present a great and ever increasing number of options which can modify the features...
Optimising non-functional properties of software is an important part of the implementation process....
Part 1: Information and Communication Technology- Eurasia Conference (ICT-EurAsia)International audi...
Compiler optimization is the technique of minimizing or maximizing some features of an executable co...
It has long been known that a fixed ordering of optimization phases will not produce the best code f...
Most applications of genetic programming (GP) involve the creation of an entirely new function, prog...
Recent research show that adaptive compiler can produce consistent improvement over a traditional fi...
Abstract—Multi-objective EAs (MOEAs) are well established population-based techniques for solving va...
In the last years, multi-objective evolutionary algorithms (MOEA) have been applied to different sof...
Automated multi-objective software optimisation offers an attractive solution to software developers...
Properly configuring Evolutionary Algorithms (EAs) is a challenging task made difficult by many diff...
The application of multi-objective evolutionary computation techniques to the genetic programming of...
International audience—Elasticity [19] is a key feature for cloud infrastruc-tures to continuously a...
The Optimization Selection Problem is widely known in computer science for its complexity and import...
A way to reduce the cost of regression testing consists of selecting or prioritizing subsets of test...
Modern compilers present a great and ever increasing number of options which can modify the features...
Optimising non-functional properties of software is an important part of the implementation process....
Part 1: Information and Communication Technology- Eurasia Conference (ICT-EurAsia)International audi...
Compiler optimization is the technique of minimizing or maximizing some features of an executable co...
It has long been known that a fixed ordering of optimization phases will not produce the best code f...
Most applications of genetic programming (GP) involve the creation of an entirely new function, prog...
Recent research show that adaptive compiler can produce consistent improvement over a traditional fi...
Abstract—Multi-objective EAs (MOEAs) are well established population-based techniques for solving va...
In the last years, multi-objective evolutionary algorithms (MOEA) have been applied to different sof...
Automated multi-objective software optimisation offers an attractive solution to software developers...
Properly configuring Evolutionary Algorithms (EAs) is a challenging task made difficult by many diff...
The application of multi-objective evolutionary computation techniques to the genetic programming of...
International audience—Elasticity [19] is a key feature for cloud infrastruc-tures to continuously a...
The Optimization Selection Problem is widely known in computer science for its complexity and import...
A way to reduce the cost of regression testing consists of selecting or prioritizing subsets of test...