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Genetic Algorithm (GA) is known as a potent multiobjective optimization method, and the effectiveness of hybridizing it with local search (LS) has recently been reported in the literature. However, there is a relatively small number of studies on LS methods for multiobjective function optimization. Although each of the existing LS methods has some strong(More)
Recent research in privacy-preserving data mining (PPDM) has become increasingly popular due to the wide application of data mining and the increased concern regarding the protection of private and personal information. Lately, numerous methods of privacy-preserving data mining have been proposed. Most of these methods are based on an assumption that(More)
In this paper, we propose a GA model called Adaptive Isolation Model(AIM), for multimodal optimization. It uses a data clustering algorithm to detect clusters in GA population, which identifies the attractors in the fitness landscape. Then, subpopulations which makes-up the clusters are isolated and optimized independently. Meanwhile, the region of the(More)
With the spread of data mining technologies and the accumulation of social data, such technologies and data are being used for determinations that seriously affect individuals' lives. For example, credit scoring is frequently determined based on the records of past credit data together with statistical prediction techniques. Needless to say, such(More)
We propose a protocol for a local search and a genetic algorithm for the distributed traveling salesman problem (TSP). In the distributed TSP, information regarding the cost function such as traveling costs between cities and cities to be visited are separately possessed by distributed parties and both are kept private each other. We propose a protocol that(More)
Although multi-objective GA (MOGA) is an efficient multi-objective optimization (MOO) method, it has some limitations that need to be tackled, which include unguaranteed uniformity of solutions and uncertain finding of periphery of Pareto-optimal solutions. It has been shown that, on bi-objective problems, which are the subject of this paper, local(More)
This paper proposes an algorithm for making recommendation so that the neutrality toward the viewpoint specified by a user is enhanced. This algorithm is useful for avoiding to make decisions based on biased information. Such a problem is pointed out as the filter bubble, which is the influence in social decisions biased by a personalization technology. To(More)