The aim of this work is to introduce an effective tool in order to help the EM designer to select the best optimization algorithm through an easy-to-manage classification of Evolutionary Algorithms. In fact, choosing the best tool for an application could be really di cult, especially for a user not aware of optimization theory. Here we propose a general analysis for EAs, highlighting their block-structure and classifying them through some objective (non-qualitative) parameters.
General structure-based classification of Optimization Algorithms for an objective comparison
NICCOLAI, ALESSANDRO;GONANO, CARLO ANDREA;GRIMACCIA, FRANCESCO;MUSSETTA, MARCO;ZICH, RICCARDO
2015-01-01
Abstract
The aim of this work is to introduce an effective tool in order to help the EM designer to select the best optimization algorithm through an easy-to-manage classification of Evolutionary Algorithms. In fact, choosing the best tool for an application could be really di cult, especially for a user not aware of optimization theory. Here we propose a general analysis for EAs, highlighting their block-structure and classifying them through some objective (non-qualitative) parameters.File in questo prodotto:
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