Abstract
Inspired by the distributed guided genetic algorithm (DGGA), D2G2A is a new multi-agent approach, which addresses Maximal Constraint Satisfaction Problems (Max-CSP). GA efficiency provides good solution quality for Max\_CSPs in one hand and benefits from multi-agent principles reducing GA temporal complexity. In addition to that the approach will be enhanced by a new parameter called guidance operator. The latter allows not only diversification but also an escaping from local optima. D2G2A and DGGA are been applied to a number of randomly generated Max\_CSPs. In order to show D2G2A advantages, experimental comparison is provided. As well, guidance operator is experimentally outlined in order to determine its best given value.
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Bouamama, S., Jlifi, B., Ghédira, K. (2003). D2G2A: A Distributed Double Guided Genetic Algorithm for Max_CSPs. In: Palade, V., Howlett, R.J., Jain, L. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2003. Lecture Notes in Computer Science(), vol 2773. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45224-9_59
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DOI: https://doi.org/10.1007/978-3-540-45224-9_59
Publisher Name: Springer, Berlin, Heidelberg
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