Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
Linkage tree genetic algorithm: first results
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
The linkage tree genetic algorithm
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Predetermined versus learned linkage models
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Evolvability analysis of the linkage tree genetic algorithm
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part I
Hierarchical problem solving with the linkage tree genetic algorithm
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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The ECGA is a competent Genetic Algorithm that uses a probabilistic model to learn the linkage among variables and then uses this information to solve hard problems using polynomial resources. However, in order to detect the linkage, the ECGA needs to perform a quadratic number of evaluations of a metric called CCC, a time consuming process. This paper presents ClusterMI, a new method for linkage detection in the ECGA. ClusterMI requires only a linear number of evaluations of the CCC reducing the overall running time of the algorithm. Experiments show that ClusterMI retains ECGA's scalability concerning population size while reducing the running time by $O(m^{0.7})$, resulting in speedups of potentially thousands of times (estimated speedup for a problem with $2^{20}$ bits is 1515).