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• W2566526863 abstract "Architecture design is one of the most important steps in software development, since design decisions affect the quality of the final system (e.g. reliability and performance). Due to the ever-growing complexity and size of software systems, deciding on the best design is a computationally intensive and complex task. This issue has been tackled by using optimisation method, such as local search and genetic algorithms. Genetic algorithms work well in rugged fitness landscapes, whereas local search methods are successful when the search space is smooth. The strengths of these two algorithms have been combined to create memetic algorithms, which have shown to be more efficient than genetic algorithms and local search on their own. A major point of concern with memetic algorithms is the likelihood of loosing the exploration capacity because of the ‘exploitative’ nature of local search. To address this issue, this work uses an adaptive scheme to control the local search application. The utilised scheme takes into account the diversity of the current population. Based on the diversity indicator, it decides whether to call local search or not. Experiments were conducted on the component deployment problem to evaluates the effectiveness of the proposed algorithm with and without the adaptive local search algorithm." @default.
• W2566526863 created "2017-01-06" @default.
• W2566526863 creator A5035346540 @default.
• W2566526863 creator A5050621611 @default.
• W2566526863 date "2016-12-27" @default.
• W2566526863 modified "2023-09-26" @default.
• W2566526863 title "An Adaptive Memetic Algorithm for the Architecture Optimisation Problem" @default.
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• W2566526863 doi "https://doi.org/10.1007/978-3-319-51691-2_22" @default.
• W2566526863 hasPublicationYear "2016" @default.
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