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Displaying 1-7 out of 7 total
Software Architecture Optimization Methods: A Systematic Literature Review
Found in: IEEE Transactions on Software Engineering
By Aldeida Aleti,Barbora Buhnova,Lars Grunske,Anne Koziolek,Indika Meedeniya
Issue Date:May 2013
pp. 658-683
Due to significant industrial demands toward software systems with increasing complexity and challenging quality requirements, software architecture design has become an important development activity and the research domain is rapidly evolving. In the las...
 
Let the Ants Deploy Your Software - An ACO Based Deployment Optimisation Strategy
Found in: Automated Software Engineering, International Conference on
By Aldeida Aleti, Lars Grunske, Indika Meedeniya, Irene Moser
Issue Date:November 2009
pp. 505-509
Decisions regarding the mapping of software components to hardware nodes affect the quality of the resulting system. Making these decisions is hard when considering the ever-growing complexity of the search space, as well as conflicting objectives and cons...
 
ArcheOpterix: An extendable tool for architecture optimization of AADL models
Found in: Model-Based Methodologies for Pervasive and Embedded Software, International Workshop on
By Aldeida Aleti, Stefan Bjornander, Lars Grunske, Indika Meedeniya
Issue Date:May 2009
pp. 61-71
For embedded systems quality requirements are equally if not even more important than functional requirements. The foundation for the fulfillment of these quality requirements has to be set in the architecture design phase. However, finding a suitable arch...
 
Characterising fitness landscapes using predictive local search
Found in: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion (GECCO '13 Companion)
By Aldeida Aleti, Irene Moser, Marius Gheorghita
Issue Date:July 2013
pp. 67-68
Search space characterisation is a field that strives to define properties of gradients with the general aim of finding the most suitable stochastic algorithms to solve the problems. Diagnostic Optimisation characterises the search landscape while the sear...
     
Entropy-based adaptive range parameter control for evolutionary algorithms
Found in: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference (GECCO '13)
By Aldeida Aleti, Irene Moser
Issue Date:July 2013
pp. 1501-1508
Evolutionary Algorithms are equipped with a range of adjustable parameters, such as crossover and mutation rates which significantly influence the performance of the algorithm. Practitioners usually do not have the knowledge and time to investigate the ide...
     
Predictive parameter control
Found in: Proceedings of the 13th annual conference on Genetic and evolutionary computation (GECCO '11)
By Aldeida Aleti, Irene Moser
Issue Date:July 2011
pp. 561-568
In stochastic optimisation, all currently employed algorithms have to be parameterised to perform effectively. Users have to rely on approximate guidelines or, alternatively, undertake extensive prior tuning. This study introduces a novel method of paramet...
     
Quality assessment of multiobjective optimisation algorithms in component deployment
Found in: Proceedings of the doctoral symposium for ESEC/FSE on Doctoral symposium (ESEC/FSE Doctoral Symposium '09)
By Aldeida Aleti
Issue Date:August 2009
pp. 171-172
Measuring the quality of the approximate sets in a quantitative way is important to asses the performance of multiobjective optimisation algorithms and decide which algorithm performs best in a problem domain. In the case of component deployment optimisati...
     
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