Crossover Operator using Knowledge Transfer for the Firefighter Problem
AbstractThis paper concerns the Firefighter Problem (FFP) which is a graph-based problem in which solutions can be represented as permutations. A new crossover operator is proposed that uses a machine learning model to decide how to combine two parent solutions of the FFP into an offspring. The operator works on two parent permutations andthe machine learning model provides information which parent to select the next permutation element from, when constructing a new solution. Training data is collected during a training run in which transpositions are applied to solutions found by an evolutionary algorithm for a small problem instance. The machine learning model is trained to classify pairs of graph vertices into two classes corresponding to which vertex should be placed earlier in the permutation. In the experiments the machine learning model was trained on a set of FFP instances with 1000 vertices. Subsequently, the proposed operator was used for solving FFP instances with up to 10000 vertices. The experiments, in which the proposed operator was compared against a set of other crossover operators, shown that the proposed operator is able to effectively use knowledge gathered when solving smaller instances for solving larger instances of the same problem
|Publication size in sheets||0.55|
|Book||Yin Hujun, Camacho David, Novais Paulo, Tallon-Ballesteros Antonio J. (eds.): Intelligent Data Engineering and Automated Learning – IDEAL 2018. 19th International Conference , Lecture Notes in Computer Science, vol. 11314, 2018, Springer International Publishing, ISBN 9783030034924, [9783030034931 ], 865 p., DOI:10.1007/978-3-030-03493-1|
|Keywords in English||Knowledge-based optimization, Graph problems, REDS graphs|
|Publication indicators||= 0|
* presented citation count is obtained through Internet information analysis and it is close to the number calculated by the Publish or Perish system.