Abstract
In this paper, we propose to integrate real coded genetic algorithm (GA) and cultural algorithms (CA) to develop a more efficient algorithm: cultural genetic algorithm (CGA). In this approach, GA’s selection and crossover operations are used in CA’s population space. GA’s mutation is replaced by CA based mutation operation which can attract individuals to move to the semifeasible and feasible region of the optimization problem to avoid the ‘eyeless’ searching in GA. Thus it is possible to enhance search ability and to reduce computational cost. This approach is applied to solve constrained optimization problems. An example is presented to demonstrate the effectiveness of the proposed approach.
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Gao, F., Cui, G., Liu, H. (2006). Integration of Genetic Algorithm and Cultural Algorithms for Constrained Optimization. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4234. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893295_90
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DOI: https://doi.org/10.1007/11893295_90
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46484-6
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