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Communication Dans Un Congrès Année : 2013

Parameter Setting with Dynamic Island Models


In this paper we proposed the use of a dynamic island model which aim at adapting parameter settings dynamically. Since each island corresponds to a specific parameter setting, measuring the evolution of islands populations sheds light on the optimal parameter settings efficiency throughout the search. This model can be viewed as an alternative adaptive operator selection technique for classic steady state genetic algorithms. Empirical studies provide competitive results with respect to other methods like automatic tuning tools. Moreover, this model could ease the parallelization of evolutionary algorithms and can be used in a synchronous or asynchronous way.

Dates et versions

hal-03256594 , version 1 (10-06-2021)



Caner Candan, Adrien Goëffon, Frédéric Lardeux, Frédéric Saubion. Parameter Setting with Dynamic Island Models. Lecture Notes in Computer Science, Learning and Intelligent Optimization (LION 7), 2013, Berlin, Heidelberg, France. ⟨10.1007/978-3-642-44973-4_26⟩. ⟨hal-03256594⟩


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