19534046
Animating the Evolution Process of Genetic Algorithms
This paper reports the work on the development of an animation system for visualising the optimisation process of the Genetic Algorithm. The description on the requirements and structure of the system is presented. The developed system is applied to visualise some six testing cases. Sequences of animation shots of the evolution process for solving the Branin RCOS problem and the Schaffer-6 problem are presented. In the latter example, the effect of a solution acceleration technique is also demonstrated. The method of building the visualisation system can be applied to other evolutionary computation techniques.
42199214
A Technique for Improving the Convergence Characteristic of Genetic Algorithms and Its Application to a Genetic-Based Load Flow Algorithm
This paper is devoted to the development of a technique for the enhancement of the convergence of genetic algorithms. Based on the concept of solution acceleration, a technique is proposed and applied to a constrained-genetic-algorithm load-flow algorithm CGALF recently developed for solving the problem of evaluating the voltage profile and power flow in electric power networks. The enhanced CGALF algorithm is applied to a practical power system to illustrate the effectiveness of the developed method.
39791908
Optimal Power Flow Method Using Evolutionary Programming
This paper reports on an evolutionary programming based method for solving the optimal power flow problem. The method incorporates an evolutionary programming based load flow solution. To demonstrate the global optimisation power of the new method it is applied to the IEEE30 bus test system with highly non-linear generator input/output cost curves and the results compared to those obtained using the method of steepest descent. The results demonstrate that the new method shows great promise for solving the optimal power flow problem when it contains highly non-linear devices.
33091738
SHAPES: A Novel Approach for Learning Search Heuristics in Under-Constrained Optimization Problems
Although much research in machine learning has been carried out on acquiring knowledge for problem-solving in many problem domains, little effort has been focused on learning search-control knowledge for solving optimization problems. This paper reports on the development of SHAPES, a system that learns heuristic search guidance for solving optimization problems in intractable, under-constrained domains based on the Explanation-Based Learning (EBL) framework. The system embodies two new and novel approaches to machine learning. First, it makes use of explanations of varying levels of approximation as a mean for verifying heuristic-based decisions, allowing heuristic estimates to be revised and corrected during problem-solving. The provision of such a revision mechanism is particularly important when working in intractable and under-constrained domains, where heuristics tend to be highly over-generalized, and hence at times will give rise to incorrect results. Second, it employs a new linear and quadratic programming-based weight-assignment algorithm formulated to direct search toward optimal solutions under best-first search. The algorithm offers a direct method for assigning rule strengths and, in so doing, avoids the need to address the credit-assignment problem faced by other iterative weight-adjustment methods.
11319559
On inserting program understanding technology into the software change process
Program understanding technologies can be applied effectively in the analysis phase of a software change process. The analysis phase naturally follows a goal driven metaprocess. Described are issues involved with inserting program understanding technology into existing practice and into such a metaprocess. The implied processes of program understanding and reverse engineering tools play an important role. These issues pose major problems for the acceptance of redocumentation tools such as Rigi, an evolvable reverse engineering tool. An example using Rigi and its analysis methodology for change impact analysis is considered.
6918962
Head Model Acquisition from Silhouettes
This paper describes a practical system developed for generating 3D models of human heads from silhouettes alone. The input to the system is an image sequence acquired from circular motion. Both the camera motion and the 3D structure of the head are estimated using silhouettes which are tracked throughout the sequence. Special properties of the camera motion and their relationships with the intrinsic parameters of the camera are exploited to provide a simple parameterization of the fundamental matrix relating any pair of views in the sequence. Such a parameterization greatly reduces the dimension of the search space for the optimization problem. In contrast to previous methods, this work can cope with incomplete circular motion and more widely spaced images. Experiments on real image sequences are carried out, showing accurate recovery of 3D shapes.
2118584
Resolution Improvement from Stereo Images with 3D Pose Differences
Resolution improvement from several images is typically restricted to simple planar rotations and translations. In this paper, a super-resolution algorithm that allows 3D ego-motion of the camera system is proposed. By exploiting the trifocal tensor constraint of a stereo camera system, the intermediate step of scene structure recovery is effectively skipped. 3D motion is estimated recursively through an extended Kalman filter and used by a novel image warping procedure to perform resolution enhancement. Real image experiment with comparison to well known Irani-Peleg approach confirms the validity of the algorithm.
12775677
Camera Pose Estimation and Reconstruction from Image Profiles under Circular Motion
This paper addresses the problem of motion estimation and reconstruction of 3D models from profiles of an object rotating on a turntable, obtained from a single camera. Its main contribution is the development of a practical and accurate technique for solving this problem from profiles alone, which is, for the first time, precise enough to allow the reconstruction of the object. No correspondence between points or lines are necessary, although the method proposed can be equally used when these features are available, without any further adaptation. Symmetry properties of the surface of revolution swept out by the rotating object are exploited to obtain the image of the rotation axis and the homography relating epipolar lines, in a robust and elegant way. These, together with geometric constraints for images of rotating objects, are then used to obtain first the image of the horizon, which is the projection of the plane that contains the camera centres, and then the epipoles, thus fully determining the epipolar geometry of the sequence of images. The estimation of the epipolar geometry by this sequential approach (image of rotation axis -- homography -- image of the horizon -- epipoles) avoids many of the problems usually found in other algorithms for motion recovery from profiles. In particular, the search for the epipoles, by far the most critical step, is carried out as a simple one-dimensional optimisation problem. The initialisation of the parameters is trivial and completely automatic for all stages of the algorithm. After the estimation of the epipolar geometry, the Euclidean motion is recovered using the fixed intrinsic parameters of the camera, obtained either from a calibration grid or from self-calibration techniques. Finally, the spinning object is reconstructed from its profiles, using the motion estimated in the previous stage. Results from real data are presented, demonstrating the efficiency and usefulness of the proposed methods.
35666520
Automatic Lyrics Alignment on Popular Music