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Erschienen in:
2011 | OriginalPaper | Buchkapitel
MetaHybrid: Combining Metamodels and Gradient-Based Techniques in a Hybrid Multi-Objective Genetic Algorithm
verfasst von : Alessandro Turco
Erschienen in: Learning and Intelligent Optimization
Verlag: Springer Berlin Heidelberg
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We propose a metamodel approach to the approximation of functions gradients within a hybrid genetic algorithm. The underlying structure is implemented in order to support parallel execution of the code: a genetic and a SQP algorithm run in different threads and can ask designs evaluations independently, but keeping all the available resources always working. A common archive collects the results and generates the population for the GA and the starting points for the SQP runs. A particular attention is dedicated to elitism and to constraints. The hybridization is performed through a modified
ε
−constrained method. The general philosophy of the algorithm is to concentrate on not wasting information: metamodels, archiving and elitism, steady-state parallel evolution are key elements for this scope and they will be discussed in details. A preliminary but explanatory row of tests concludes the paper highlighting the benefits of this new approach.