Kaikuo Xu

A novel method for real parameter optimization based on Gene Expression Programming

Gene Expression Programming (GEP) is a new technique of evolutionary algorithm that implements genome/phoneme representation in computing programs. Due to its power in global search, it is widely applied in symbolic regression. However, little work has been done to apply it to real parameter optimization yet. This paper proposes a real parameter optimization method named Uniform-Constants based GEP (UC-GEP). In UC-GEP, the constant domain directly participates in the evolution. Our research conducted extensive experiments over nine benchmark functions from the IEEE Congress on Evolutionary Computation 2005 and compared the results to three other algorithms namely Meta-Constants based GEP (MC-GEP), Meta-Uniform-Constants based GEP (MUC-GEP), and the Floating Point Genetic Algorithm (FP-GA). For simplicity, all GEP methods adopt a one-tier index gene structure. The results demonstrate the optimal performance of our UC-GEP in solving multimodal problems and show that at least one GEP method outperforms FP-GA on all test functions with higher computational complexity.

Optimal Multiple Sink Nodes Deployment in Wireless Sensor Networks Based on Gene Expression Programming

In wireless sensor networks (WSN) data transmission is usually performed by sensors in manner of multi-hop forwarding towards a central static control center (sink). A lot of cheap, low-powered and energy-limited sensors are deployed in the monitored area and some of these nodes closer to the sink node use up their energy more quickly than other nodes because they relay more packets. Although most of the sensor nodes have enough energy left to work, the energy consumption imbalance leads to connectivity holes and coverage holes, and finally the whole network failure. The main contributions of this paper include: (a) a new scheme based on multiple sink nodes is proposed to prolong the network lifetime and to reduce the response time. It is effective, especially in the target tracking applications, (b) the deployment strategy with given number of multiple sink nodes is explored in the grid sensor network, (c) Gene Expression Programming based Multiple Sink Nodes deployment algorithm (GEP-MSN) is proposed to optimally deploy multiple sink nodes over the monitored region, (d) a data transmission cost model (TCM) is introduced to measure the cost for optimizing during the transmission phase, (e) extensive simulations are conducted to show that the scheme can greatly extend the network lifetime by around 16.6% and 36.3% on average compared with two naive methods based on random distributed sink nodes.

A Comparative Study of Six Software Packages for Complex Network Research

UCINET, Pajek, Networkx, iGraph, JUNG and statnet, are commonly used to perform analysis with complex network model. The scalability, and function coverage of these six software packages are assessed and compared. Some randomly generated datasets are used to evaluate the performance of these software packages with regard to input/output (I/O), basic graph algorithms, statistical metrics computation, graph generation, community detection, and visualization. A metric regarding both numbers of the nodes and the edges of complex networks, which is called Maximum Expected Network Processing Ability (MENPA), is proposed to measure the scalability of software packages. Empirical results show that these six software packages are complementary rather than competitive and the difference on the scalability among these six software packages may be attributed to the varieties in both of the programming languages and the network representations.

A Comparative Study of Correlation Measurements for Searching Similar Tags

In recent years, folksonomy becomes a hot topic in many research fields such as complex systems, information retrieval, and recommending systems. It is essential to study the semantic relationships among tags in folksonomy applications. The main contributions of this paper includes: (a) proposes a general framework for the analysis of the semantic relationships among tags based on their co-occurrence. (b)investigates eight correlation measurements from various fields; then appliying these measurements to searching similar tags for a given tag on datasets from del.icio.us. (c) conducts a comparative study on both accuracy and time performance of the eight measurements. From the comparison, a best overall correlation measurement is concluded for similar tags searching in the applications of folksonomy.

An intelligent semantic-based tag cleaner for folksonomies

The collaborative tagging provided by folksonomy systems is an un-controlled process for users, and the personal and arbitrary tag assignments lead to great tag noises. To solve the problem, authors make contributions as follows: (a) demonstrate that tags assigned to web resources are highly noisy due to the diverse un-controlled present styles of tags; (b) present a two-stage method to clean syntactic and semantic tag noises by taking semantic as the relevance measurement for tags; (c) conduct extensive experiments using dataset collected from del.icio.us. The ratio of the noise tags discovered by our method is up to 40%, and the experiment results show that the proposed method in either semantic approach is highly effective.

CTSC: Core-Tag Oriented Spectral Clustering Algorithm on Web2.0 Tags

With the rapid development of the Web2.0communities, many researchers have been attracted by the concept of folksonomy from the field of data mining and information retrieval. Finding out semantic correlation of tags is avid requirement for web2.0application. However, no proper algorithm can tackle this task very well. This paper proposes a core-tag oriented clustering method to handle the task. The main contributions include: (1) Proposing the concept of core-tag oriented space; (2) Proposing a method called Core-Tag oriented Spectral Clustering (CTSC) to cluster tags in the new space; (3) Designing experiments to evaluate the algorithm, and the results show that CTSC algorithm performs well on clustering tags.

Core-Tag Clustering for Web 2.0 Based on Multi-similarity Measurements

Along with the development of Web2.0, folksonomy has become a hot topic related to data mining, information retrieval and social network. The tag semantic is the key for deep understanding the correlation of objects in folksonomy. This paper proposes two methods to cluster tags for core-tag by fusing multi-similarity measurements. The contributions of this paper include: (1) Proposing the concept of core-tag and the model of core-tag clusters. (2) Designing a core-tag clustering algorithm CETClustering, based on clustering ensemble method. (3) Designing a second kind of core-tag clustering algorithm named SkyTagClustering, based on skyline operator. (4) Comparing the two algorithms with modified K-means. Experiments show that the two algorithms are better than modified K-means with 20-30% on efficiency and 20% higher scores on quality.

Application of Gene Expression Programming to Real Parameter Optimization

Gene Expression Programming (GEP) is a new evolutionary algorithm that implements genome/phoneme representations. Despite its powerful global search ability and wide application in symbolic regression, little work has been done to apply it to real parameter optimization. A real parameter optimization method named Uniform-Constant based GEP (UC-GEP) is proposed in this paper. The main work and contributions include: (1) Compares UC-GEP with Meta-Constant based GEP (MC-GEP), Meta-Uniform-Constant based GEP (MUC-GEP), and Floating Point Genetic Algorithm (FP-GA) on optimizing seven benchmark functions, respectively. Experiment results show that GEP methods outperform FP-GA on five of the seven functions and UC-GEP reaches the global optimum on all seven functions. (2) Compares UC-GEP with both MC-GEP and MUC-GEP on optimizing Rastrigin and Griewangk with various dimensions. Experiment results also show that UC-GEP is the best among these three algorithms.

SHG-Tree: an efficient granularity-based spatial index structure

To improve the access efficiency of multidimensional spatial database, this study proposes a new index structure named Space Hypercube Grid Tree (SHG-Tree). By avoiding the problems of node split and recombination, SHG-Tree can efficiently support the common operations over spatial database containing objects with dynamic region. The main contributions of this paper include: (1) Proposes SHG-Tree of n-dimensional space with a hierarchical tree structure. It reflects the region overlapping relationship of hypercube grid units with different granularity. (2) Proposes the linearization methods to present the bounding rectangle of object as a union of variant granularity hypercube grids. (3) Gives operations of SHG-Tree. Experiments result shows the size of SHG-Tree is small enough to remain in main memory even to very large spatial database by applying proper linearization strategy and the queries on SHG-Tree are less than ten milliseconds to ensure the real-time of query.

Sub-frequent Patterns Mining Based on SFP-Tree

Resource distribution optimization and load balance of distributed P2P network can be described as mining sub-frequent patterns (SFPs) from query response transaction database. The response of a latter query for a file resource provided by P2P system will pick out one SFP from the SFPs of this file resource and returns a subset of selected SFP. To realize the SFPs mining this paper proposes the structure of SFP-tree along with relative mining algorithms. The main content includes: proposing the concept of sub-frequent pattern; proposing the SFP-tree along with frequency-ascending order header table FP-Tree (AFP-Tree) and conditional mix pattern tree (CMP-Tree); and proposing the SFPs mining algorithms base on SFP-Tree. The performance experiment shows the effectiveness and efficiency of SFP-Tree based mining algorithm.