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In the biological and biomedical research field, Microarray technology has grown into a leading approach. Microarray technology helps to monitor a large number of genes simultaneously based on different experimental conditions. This paper propose a fast elitist non-dominated sorting multi-objective genetic algorithm (NSGA-II) based fuzzy relational clustering approach for clustering microarray cancer...
Effective testing is essential for assuring software quality. While regression testing is time-consuming, the fault detection capability may be compromised if some test cases are discarded. Test case prioritization is a viable solution. To the best of our knowledge, the most effective test case prioritization approach is still the additional greedy algorithm, and existing search-based algorithms have...
Within a large group of decision makers, varying amounts of both conflicting and harmonious views will be prevalent within the group, but obscured due to group size. When the number of Decision Makers is large, utilizing clustering during the process of aggregation of their views should aid both knowledge discovery - about the group's conflict and consensus - as well as helping to streamline the aggregation...
Flexible job shop scheduling problem (FJSP) is an important extension of the classical job shop scheduling problem, where each operation could be processed on more than one machine and vice versa. Since it has been proven that this problem is strongly NP-hard, it is difficult to achieve an optimal solution with traditional optimization algorithms. In this paper a new approach is proposed to solve...
In order to further ease the disaster of computing costs in multi-objective optimization problem, we've put forward a kind of multi-objective genetic algorithm based on clustering. The algorithm uses the fuzzy c-means clustering control the similar individuals gathered in a class and for each class construct non-dominated set with arena's principle, so that we can use faster speed to choose the non-dominated...
The crossover operator plays an important role in a genetic algorithm, which produces two or more offspring for each pair of parents. With the help of the crossover operator, the genetic algorithm can explore the search space effectively. In this paper, we propose a new crossover operator called elliptical crossover operator, which can explore the search domain effectively. A local search scheme is...
In this paper, a multi-objective genetic algorithm for data clustering based on the robust fuzzy least trimmed squares estimator is proposed. The clustering methodology addresses two critical issues in unsupervised data clustering - the ability to produce meaningful classification in noisy data, and the requirement that the number of clusters be known a priori. The GA-driven clustering routine optimizes...
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