Distributed island-model genetic algorithms using heterogeneous parameter settings

2011 ◽  
Author(s):  
Yiyuan Gong ◽  
Alex Fukunaga
Keyword(s):  
Genetic Algorithms ◽  
Island Model

Parallel Island Model Genetic Algorithms applied in NP-Hard problems

2019 ◽  
Author(s):  
Lucas A. da Silveira ◽  
Jose L. Soncco-Alvarez ◽  
Thaynara A. de Lima ◽  
Mauricio Ayala-Rincon
Keyword(s):  
Genetic Algorithms ◽  
Island Model ◽  
Np Hard ◽  
Hard Problems ◽  
Np Hard Problems

scholarly journals Using Hadoop MapReduce for Parallel Genetic Algorithms: A Comparison of the Global, Grid and Island Models

2018 ◽  
Vol 26 (4) ◽  
pp. 535-567 ◽  
Author(s):  
Filomena Ferrucci ◽  
Pasquale Salza ◽  
Federica Sarro
Keyword(s):  
Genetic Algorithms ◽  
Parallel Algorithms ◽  
Execution Time ◽  
Island Model ◽  
Island Models ◽  
Parallel Genetic Algorithms ◽  
Hadoop Mapreduce ◽  
Data Store ◽  
Parallel Models ◽  
Global Grid

The need to improve the scalability of Genetic Algorithms (GAs) has motivated the research on Parallel Genetic Algorithms (PGAs), and different technologies and approaches have been used. Hadoop MapReduce represents one of the most mature technologies to develop parallel algorithms. Based on the fact that parallel algorithms introduce communication overhead, the aim of the present work is to understand if, and possibly when, the parallel GAs solutions using Hadoop MapReduce show better performance than sequential versions in terms of execution time. Moreover, we are interested in understanding which PGA model can be most effective among the global, grid, and island models. We empirically assessed the performance of these three parallel models with respect to a sequential GA on a software engineering problem, evaluating the execution time and the achieved speedup. We also analysed the behaviour of the parallel models in relation to the overhead produced by the use of Hadoop MapReduce and the GAs' computational effort, which gives a more machine-independent measure of these algorithms. We exploited three problem instances to differentiate the computation load and three cluster configurations based on 2, 4, and 8 parallel nodes. Moreover, we estimated the costs of the execution of the experimentation on a potential cloud infrastructure, based on the pricing of the major commercial cloud providers. The empirical study revealed that the use of PGA based on the island model outperforms the other parallel models and the sequential GA for all the considered instances and clusters. Using 2, 4, and 8 nodes, the island model achieves an average speedup over the three datasets of 1.8, 3.4, and 7.0 times, respectively. Hadoop MapReduce has a set of different constraints that need to be considered during the design and the implementation of parallel algorithms. The overhead of data store (i.e., HDFS) accesses, communication, and latency requires solutions that reduce data store operations. For this reason, the island model is more suitable for PGAs than the global and grid model, also in terms of costs when executed on a commercial cloud provider.

Development of Island Model Genetic Algorithms with Different Genetic Operators

2021 ◽  
Vol 141 (12) ◽  
pp. 1430-1436
Author(s):  
Tomohiro Hayashida ◽  
Ichiro Nishizaki ◽  
Shinya Sekizaki ◽  
Hirotake Mochida
Keyword(s):  
Genetic Algorithms ◽  
Island Model ◽  
Genetic Operators
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