In this study, we experiment with several multiobjective evolutionary algorithms to determine a suitable approach for clustering Web user sessions, which consist of sequences of Web pages visited by the users. Our experimental results show that the multiobjective evolutionary algorithm-based approaches are successful for sequence clustering. We look at a commonly used cluster validity index to verify our findings. The results for this index indicate that the clustering solutions are of high quality. As a case study, the obtained clusters are then used in a Web recommender system for representing usage patterns. As a result of the experiments, we see that these approaches can successfully be applied for generating clustering solutions that lead to a high recommendation accuracy in the recommender model we used in this paper.

Attractiveness of social network analysis as a research topic in many different disciplines is growing in parallel to the continuous growth of the Internet, which allows people to share and collaborate more. Nowadays, detection of community structures, which may be established on social networks, is a popular topic in Computer Science. High computational costs and non-scalability on large-scale social networks are the biggest drawbacks of popular community detection methods. The main aim of this study is to reduce the original network graph to a maintainable size so that computational costs decrease without loss of solution quality, thus increasing scalability on such networks. In this study, we focus on Ant Colony Optimization techniques to find quasicliques in the network and assign these quasi-cliques as nodes in a reduced graph to use with community detection algorithms. Experiments are performed on commonly used social networks with the addition of several large-scale networks. Based on the experimental results on various sized social networks, we may say that the execution times of the community detection methods are decreased while the overall quality of the solution is preserved.

Clustering methods provide users with methods to summarize and organize the huge amount of data in order to help them find what they are looking for. However, one of the drawbacks of clustering algorithms is that the result may vary greatly when using different clustering criteria. In this paper, we present a new clustering algorithm based on graph partitioning approach that only considers the pairwise similarities. The algorithm makes no assumptions about the size or the number of clusters. Besides this, the algorithm can make use of multiple clustering criteria functions. We will present experimental results on a synthetic data set and a real world web log data. Our experiments indicate that our clustering algorithm can efficiently cluster data items without any constraints on the number of clusters.

Abstract. Hyper-heuristics are high level methodologies that perform search over the space of heuristics rather than solutions for solving computationally difficult problems. A selection hyper-heuristic framework provides means to exploit the strength of multiple low level heuristics where each heuristic can be useful at different stages of the search. In this study, the behavior of a range of selection hyper-heuristics is investigated in dynamic environments. The results show that hyper-heuristics embedding learning heuristic selection methods are sufficiently adaptive and can respond to different types of changes in a dynamic environment.

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In web recommender systems, clustering is done offline to extract usage patterns and a successful recommendation highly depends on the quality of this clustering solution. In these types of applications, data to be clustered is in the form of user sessions which are sequences of web pages visited by the user. Sequence clustering is one of the important tools to work with this type of data. One way to represent sequence data is through weighted, undirected graphs where each sequence is a vertex and the pairwise similarities between the user sessions are the edges. Through this representation, the problem becomes equivalent to graph partitioning which is NP-complete and is best approached using multiple objectives. Hence it is suitable to use multiobjective evolutionary algorithms (MOEA) to solve it. The main focus of this paper is to determine an effective MOEA to cluster sequence data. Several existing approaches in literature are compared on sample data sets and the most suitable approach is determined.

Many real-world optimization problems are dynamic in nature. The interest in the Evolutionary Algorithms (EAs) community in applying EA variants to dynamic optimization problems has increased greatly. Differential Evolution (DE) belongs to the group of evolutionary algorithms which operate in continuous search spaces. DE has been successfully applied to many stationary problem domains. Recently there has been some research into applying DE to dynamic optimization problems too. Many real-world problems consist of decision variables which require the optimization algorithm to work with binary parameters. This makes it impossible to apply DE in its basic form. For this purpose, binary differential evolution (BDE) approaches have been introduced. The main focus of this paper is to perform a series of experiments to test the behavior of a simple BDE under different change conditions. A simple bit-matching problem is chosen as the test environment. The results of this preliminary study show that further study is needed to make BDEs suitable to work in dynamic environments.