Genetic algorithms are powerful search algorithms that can be applied to a wide range of problems. Generally, parameter setting is accomplished prior to running a Genetic Algorithm (GA) and this setting remains unchanged during execution. The problem of interest to us here is the self-adaptive parameters adjustment of a GA. In this research, we propose an approach in which the control of a genetic algorithm’s parameters can be encoded within the chromosome of each individual. The parameters’ values are entirely dependent on the evolution mechanism and on the problem context. Our preliminary results show that a GA is able to learn and evaluate the quality of self-set parameters according to their degree of contribution to the resolution of the problem. These results are indicative of a promising approach to the development of GAs with self-adaptive parameter settings that do not require the user to pre-adjust parameters at the outset.
The design of an efficient machine learning process through self-adaptation is a great challenge. The goal of meta-learning is to build a self-adaptive learning system that is constantly adapting to its specific (and dynamic) environment. To that end, the meta-learning mechanism must improve its bias dynamically by updating the current learning strategy in accordance with its available experiences or meta-knowledge. We suggest using genetic algorithms as the basis of an adaptive system. In this work, we propose a meta-learning system based on a combination of the a priori and a posteriori concepts. A priori refers to input information and knowledge available at the beginning in order to built and evolve one or more sets of parameters by exploiting the context of the system’s information. The self-learning component is based on genetic algorithms and neural Darwinism. A posteriori refers to the implicit knowledge discovered by estimation of the future states of parameters and is also applied to the finding of optimal parameters values. The in-progress research presented here suggests a framework for the discovery of knowledge that can support human experts in their intelligence information assessment tasks. The conclusion presents avenues for further research in genetic algorithms and their capability to learn to learn.
In the field of pattern recognition from satellite images, the existing road extraction methods have been either too specialized or too time consuming. The challenge then has been to develop a general and close to real time road extraction method. This study falls in this perspective and aims at developing a close to real time semi-automatic system able to extract linear planimetric features (including roads). The major concern of this study is to combine the most efficient tools to deal with the road primitive extraction process in order to handle multi- resolution and multi-type raw images. Hence, this study brought along a new model fusion characterized by the combination of operator input points (in 2D or 3D), fuzzy image filtering, cubic natural splines and the A*algorithm. First, a cubic natural splines interpolation of the operator points is used to parameterize the A*algorithm. Cost function with the consequence to restrict the mining research area. Second, the heuristic function of the same algorithm is combined with the fuzzy filtering which proves to be a fast and efficient tool for selection of the primitive most promising points. The combination of the cost function and the heuristic function leads to a limited number of hypothetical paths, hence decreasing the computation time. Moreover, the combination of the A*algorithm and the splines leads to a new way to solve the perceptual grouping problems. Results related to the problem of feature discontinuity suggest new research perspectives in relation to noisy area (urban) as well as noisy data (radar images).
The development of efficient semi-automatic systems for heterogeneous information fusion is actually a great challenge. The efficiency can be presented as the system openness, the system evolution capabilities and the system performance. Multi- agent architecture can be designed in order to respect the first two efficiency constraints. As for the third constraint, which is the performance, the key point is the interaction between each information component of the system. The context of this study is the development of a semi-automatic information fusion system for cartographic features interpretation. Combining heterogeneous sources of information such as expert rules and strategies, domain models, image processing tools, interpolation techniques, etc. completes the system development task. The information modeling and fusion is performed within the evidential theory concepts. The purpose of this article is to propose a learning approach for interaction-oriented multi-agent systems. The optimization of the interaction weight is tackled with genetic algorithms technique because it provides solution for the whole set of weights at once. In this paper, the context of the multi-agent system development is presented first. The need for such system and its parameters is explained. A brief overview of learning techniques leads to genetic algorithms as a choice for the learning of the developed multi-agent system. Two approaches are designed to measure the system's fitness based on either binary or fuzzy decisions. The conclusion presents suggestions for further research in the area of multi-agent system-learning with genetic algorithms.
The availability of multi-sensed data, especially in remote sensing, leads to new possibilities in the area of target recognition. In fact, the information contained in an individual sensor represents only one facet of the reality. The use of several sensors aims at covering different facets of real world objects. In this study, the targets to recognize are the planimetric features (i.e. roads, energy transmission lines, railroads and rivers). The sensors used are visible type satellite sensors (SPOT Panchromatic and Landsat TM) as well as radar satellites (Radarsat fine mode and ERS-1). Sensor resolutions range from 8 to 30 meters/pixel. In this study, the modeling is not limited, as it is generally the case, to the problem feature's reality, but to each sensor that will be used. Moreover, the decision space (here a 3D symbolic map) has to be modeled in the same way as the reality and sensors to lead to a coherent and uniform system. Each model is developed using an object- oriented approach. Each reality-object is defined through its radiometric, geometric and topologic feature. The sensor model objects are defined in accordance to image acquisition and definition, including the stereo image cases (for SPOT and Radarsat). Finally, the decision space objects define the resulting 3D symbolic map where, for instance, a pixel attributes contain classification information as well as position, accuracy, reality object's membership values, etc.
This study suggests a slight variation of the Dempster- Shafer theory using observation qualification in multi- sensor contexts. The uncertainty is placed on the rules instead of on sources. Thus, sensor's specialization is taken into account. By this approach, the masses are not directly attributed on the frame of discernment elements, but on the rules themselves that become the sources of knowledge, in the context of Dempster combining rule. It proposes then an approach for observation qualification in a multi-sensor context, as well as it suggests a new path for the delicate task of mass attribution.
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