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Genetic Algorithms (GAs) have been widely applied in Steiner tree optimization problems. However, as the core operation, existing crossover operators for tree-based GAs suffer from producing illegal offspring trees. Therefore, some global link information must be adopted to ensure the connectivity of the offspring, which incurs heavy computation. To address this problem, this paper proposes a new crossover mechanism, called Leaf Crossover, which generates legal offspring by just exchanging partial parent chromosomes, requiring neither the global network link information, encoding/decoding nor repair operations. Our simulation study indicates that GAs with leaf crossover outperform GAs with existing crossover mechanisms in terms of not only producing better solutions but also converging faster in networks of varying sizes.

Using imbalanced data in classification affect the accuracy. If the classification is based on imbalanced data directly, the results will have large deviations. A common approach to dealing with imbalanced data is to re-structure the raw dataset via undersampling method. The undersampling method usually uses random or clustering approaches to trimming the majority class in the dataset, since some data in the majority class makes not contribute to classification model. In this paper a revised undersampling approach is proposed. First, we perform space compression in the vertical direction of the separating hyperplane. Then, a weighted random sampling hybrid ensemble learning method is carried out to make the sampled objects spread more widely near the separating hyperplane. Experiments with 7 under-sampling methods on 21 imbalanced datasets show that our method has achieved good results.

In this paper, we propose a genetic evolutionary ROCK algorithm (GE-ROCK). GE-ROCK is an improved ROCK algorithm which combines the techniques of clustering and genetic optimization. Genetic optimization is exploited here to improve the clustering process. In GE-ROCK, similarity function is used throughout the iterative clustering process, while in the "conventional" ROCK algorithm, similarity function is only to be used for the initial calculation. To evaluate the performance of the GE-ROCK, we exploit the well-known voting data sets. A comparative analysis demonstrates that the GE-ROCK leads to the superior performance not only better clustering quality but also shorter computing time when comparing the ROCK algorithm commonly used in the literature.