Abstract
In the firefighter problem the spread of fire is modelled on an undirected graph. The goal is to find such an assignment of firefighters to the nodes of the graph that they save as large part of the graph as possible.
In this paper a multi-objective version of the firefighter problem is proposed and solved using an evolutionary algorithm. Two different auto-adaptation mechanisms are used for genetic operators selection and the effectiveness of various crossover and mutation operators is studied.
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Michalak, K. (2014). Auto-adaptation of Genetic Operators for Multi-objective Optimization in the Firefighter Problem. In: Corchado, E., Lozano, J.A., Quintián, H., Yin, H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2014. IDEAL 2014. Lecture Notes in Computer Science, vol 8669. Springer, Cham. https://doi.org/10.1007/978-3-319-10840-7_58
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DOI: https://doi.org/10.1007/978-3-319-10840-7_58
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