Hybridizing a genetic algorithm with an artificial immune system for global optimization

Hybridizing a genetic algorithm with an artificial immune system for global optimization

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Article ID: iaor20051530
Country: United Kingdom
Volume: 36
Issue: 5
Start Page Number: 607
End Page Number: 634
Publication Date: Oct 2004
Journal: Engineering Optimization
Authors: ,
Keywords: genetic algorithms, global optimization
Abstract:

This paper proposes an algorithm based on a model of the immune system to handle constraints of all types (linear, nonlinear, equality, and inequality) in a genetic algorithm used for global optimization. The approach is implemented both in serial and parallel forms, and it is validated using several test functions taken from the specialized literature. Our results indicate that the proposed approach is highly competitive with respect to penalty-based techniques and with respect to other constraint-handling techniques which are considerably more complex to implement.

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