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The following topics are dealt with: machine learning; swarm intelligence; constraint programming; evolutionary algorithms; data mining; uncertainty handling; natural language processing; image processing; robotics; and multiagent systems.
Classification is a widely used mechanism for facilitating Web service discovery. Existing methods for automatic Web service classification only consider the case where the category set is small. When the category set is big, the conventional classification methods usually require a large sample collection, which is hardly available in real world settings. This paper presents a novel method to conduct...
This paper presents an off-line signature verification system composed of a combination of several different classifiers. Identity authentication is a very important characteristics specially in systems that requires a high degree of security such as in bank transactions. In our experiments, one-class classifier was used to create a signature verification system, consequently only genuine signatures...
K-Means algorithm is one of the most used clustering algorithm for Knowledge Discovery in Data Mining. Seed based K-Means is the integration of a small set of labeled data (called seeds) to the K-Means algorithm to improve its performances and overcome its sensitivity to initial centers. These centers are, most of the time, generated at random or they are assumed to be available for each cluster....
In this paper we introduce an ant-based algorithm on continuous domains used to create procedural animations. We focus our approach on finding movement sequences that satisfy both the physical constraints as well as animator requirements. The proposed method is very flexible and can be easily adapted to different situations and characters' morphologies. Our simulations show that the proposed technique...
Traffic congestion has been a long-held social problem because of the increasing number of vehicles. In this paper, we apply a pheromone model to a traffic signal control in order to alleviate the traffic congestion. The pheromone model is a tool to communicate among insects (e.g., ants) for their crowd action. In our target problem, the pheromone is strewed by vehicles in a trail across the road...
One of the well studied issues in multiagent systems is the action-selection and sequencing problem where a goal is decomposed in tasks that can be performed in different ways and/or by different agents. This problem has been tackled under different approaches. In particular, for open, dynamic environments agents must be able to adapt to the changing organizational goals, available resources, their...
When a local optimal solution is reached with classical Particle Swarm Optimization (PSO), all particles in the swarm gather around it, and escaping from this local optima becomes difficult. To avoid premature convergence of PSO, we present in this paper a novel variant of PSO algorithm, called MPSOM, that uses Metropolis equation to update local best solutions (lbest) of each particle and uses mutation...
It is well known that modeling with constraints networks require a fair expertise. Thus tools able to automatically generate such networks have gained a major interest. The major contribution of this paper is to set a new framework based on Inductive Logic Programming able to build a constraint model from solutions and non-solutions of related problems. The model is expressed in a middle-level modeling...
This work presents the concept of Continuous Search (CS), which objective is to allow any user to eventually get their constraint solver achieving a top performance on their problems. Continuous Search comes in two modes: the functioning mode solves the user's problem instances using the current heuristics model; the exploration mode reuses these instances to train and improve the heuristics model...
Constraint programs such as those written in high level modeling languages (e.g., OPL, ZINC, or COMET) must be thoroughly verified before being used in applications. Detecting and localizing faults is therefore of great importance to lower the cost of the development of these constraint programs. In a previous work, we introduced a testing framework called CPTEST enabling automated test case generation...
The existence of powerful constraint satisfaction algorithms is not the sole reason of the wide success of the CSP framework. The interest of this framework is also that it offers a generic and simple way for the modeling of real world applications. Nevertheless these applications call for tasks that often differ from a classical search for a solution. The aim of the present paper is not to provide...
This paper presents Perturbed Frequent Itemset based Classification Technique (PERFICT), a novel associative classification approach based on perturbed frequent itemsets. Most of the existing associative classifiers work well on transactional data where each record contains a set of boolean items. They are not very effective in general for relational data that typically contains real valued attributes...
This work studies the use of Particle Swarm Optimization (PSO) as a classification technique. Beyond assessing classification accuracy, it investigates the following questions: does PSO present limitations for high dimensional application domains? Is it less efficient for multi class problems? To answer the questions, an experimental set up was realized that uses three high dimensional data sets....
Most classification studies are done by using all the objective data. It is expected to classify objects by using some subsets data effectively. A rough set based reduct is a minimal subset of features, which has almost the same discernible power as the entire features. Here, we propose multiple reducts which are followed by the k-nearest neighbor with confidence to classify documents with higher...
Detecting communities from complex networks has triggered considerable attention in several application domains. Targeting this problem, a local search based genetic algorithm (GALS) which employs a graph-based representation (LAR) has been proposed in this work. The core of the GALS is a local search based mutation technique. Aiming to overcome the drawbacks of the existing mutation methods, a concept...
A great number of techniques were already applied to the non-adversarial variation of the multiagent patrolling problem. Experiments suggest that, for general graphs, all those approaches are inferior to a strategy based on the travelling salesman problem (TSP), in which agents are distributed equidistantly along the TSP-cycle. This approach, however, is neither optimal nor scalable. In this article,...
The balanced graph partitioning consists in dividing the vertices of an undirected graph into a given number of subsets of approximately equal size, such that the number of edges crossing the subsets is minimized. In this work, we present a multilevel memetic algorithm for this NP-hard problem that relies on a powerful grouping recombination operator and a dedicated local search procedure. The proposed...
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