s: Jan. 1992 -- Dec. 1994 ffl CTI: Current Technology Index Jan./Feb. 1993 -- Jan./Feb. 1994 ffl DAI: Dissertation Abstracts International: Vol. 53 No. 1 -- Vol. 55 No. 4 (1994) ffl EEA: Electrical & Electronics Abstracts: Jan. 1991 -- Dec. 1994 ffl P: Index to Scientific & Technical Proceedings: Jan. 1986 -- Feb. 1995 (except Nov. 1994) ffl EI A: The Engineering Index Annual: 1987 -- 1992 ffl EI M: The Engineering Index Monthly: Jan. 1993 -- Dec. 1994 The following GA researchers have already kindly supplied their complete autobibliographies and/or proofread references to their papers: Dan Adler, Patrick Argos, Jarmo T. Alander, James E. Baker, Wolfgang Banzhaf, Ralf Bruns, I. L. Bukatova, Thomas Back, Yuval Davidor, Dipankar Dasgupta, Marco Dorigo, Bogdan Filipic, Terence C. Fogarty, David B. Fogel, Toshio Fukuda, Hugo de Garis, Robert C. Glen, David E. Goldberg, Martina Gorges-Schleuter, Jeffrey Horn, Aristides T. Hatjimihail, Mark J. Jakiela, Richard S. Judson, Akihiko Konaga...

Genetic and evolutionary algorithms have been increasingly applied to solve complex, large scale search problems with mixed success. Competent genetic algorithms have been proposed to solve hard problems quickly, reliably and accurately. They have rendered problems that were difficult to solve by the earlier GAs to be solvable, requiring only a subquadratic number of function evaluations. To facilitate solving large-scale complex problems, and to further enhance the performance of competent GAs, various efficiency-enhancement techniques have been developed. This study investigates one such class of efficiency-enhancement technique called evaluation relaxation. Evaluation-relaxation schemes replace a high-cost, low-error fitness function with a low-cost, high-error fitness function. The error in fitness functions comes in two flavors: Bias and variance. The presence of bias and variance in fitness functions is considered in isolation and strategies for increasing efficiency in both cases are developed. Specifically, approaches for choosing between two fitness functions with either differing variance or differing bias values have been developed. This thesis also investigates fitness inheritance as an evaluation-

We present a novel evolutionary approach to robotic control of a real robot based on genetic programming (GP). Our approach uses genetic programming techniques that manipulate machine code to evolve control programs for robots. This variant of GP has several advantages over a conventional GP system, such as higher speed, lower memory requirements and better real time properties. Previous attempts to apply GP in robotics use simulations to evaluate control programs and have difficulties with learning tasks involving a real robot. We present an on-line control method that is evaluated in two different physical environments and applied to two tasks using the Khepera robot platform: obstacle avoidance and object following. The results show fast learning and good generalization.

We investigate the application of genetic algorithms (GAs) for recognizing real two-dimensional (2-D) or three-dimensional (3-D) objects from 2-D intensity images, assuming that the viewpoint is arbitrary. Our approach is model-based (i.e., we assume a predefined set of models), while our recognition strategy lies on the recently proposed theory of algebraic functions of views. According to this theory, the variety of 2-D views depicting an object can be expressed as a combination of a small number of 2-D views of the object. This implies a simple and powerful strategy for object recognition: novel 2-D views of an object (2-D or 3-D) can be recognized by simply matching them to combinations of known 2-D views of the object. In other words, objects in a scene are recognized by "predicting" their appearance through the combination of known views of the objects. This is an important idea, which is also supported by psychophysical findings indicating that the human visual system works in a similar way. The main difficulty in implementing this idea is determining the parameters of the combination of views. This problem can be solved either in the space of feature matches among the views ("image space") or the space of parameters ("transformation space"). In general, both of these spaces are very large, making the search very time consuming. In this paper, we propose using GAs to search these spaces efficiently. To improve the efficiency of genetic search in the transformation space, we use singular value decomposition and interval arithmetic to restrict genetic search in the most feasible regions of the transformation space. The effectiveness of the GA approaches is shown on a set of increasingly complex real scenes where exact and near-exact matches are found reliably and q...

We consider the problem of gender classification from frontal facial images using genetic feature subset selection. We argue that feature selection is an important issue in gender classification and demonstrate that Genetic Algorithms (GA) can select good subsets of features (i.e., features that encode mostly gender information), reducing the classification error. First, Principal Component Analysis (PCA) is used to represent each image as a feature vector (i.e., eigen-features) in a low-dimensional space. Genetic Algorithms (GAs) are then employed to select a subset of features from the low-dimensional representation by disregarding certain eigenvectors that do not seem to encode important gender information. Four different classifiers were compared in this study using genetic feature subset selection: a Bayes classifier, a Neural Network (NN) classifier, a Support Vector Machine (SVM) classifier, and a classifier based on Linear Discriminant Analysis (LDA). Our experimental results show a significant error rate reduction in all cases. The best performance was obtained using the SVM classifier. Using only 8.4% of the features in the complete set, the SVM classifier achieved an error rate of 4.7% from an average error rate of 8.9% using manually selected features.

Genetic algorithms (GAs) are search methods based on principles of natural selection and genetics (Fraser, 1957; Bremermann, 1958; Holland, 1975). We start with a brief introduction to simple genetic algorithms and associated terminology. GAs encode

HIGH-PERFORMANCE AUTOMATIC IMAGE REGISTRATION FOR REMOTE SENSING Prachya Chalermwat, Ph.D. George Mason University, 1999 Thesis Director: Dr. Tarek El-Ghazawi Image registration is one of the crucial steps in the analysis of remotely sensed data. A new acquired image must be transformed, using image registration techniques, to match the orientation and scale of previous related images. Image registration requires intensive computational effort not only because of its computational complexity, but also due to the continuous increase in image resolution and spectral bands. Thus, high-performance computing techniques for image registration are critically needed. Very few works have addressed image registration on contemporary highperformance computing systems. Furthermore, issues of load balancing, scalability, and formal analysis of algorithmic efficiency were seldom considered. iv This dissertation introduces high-performance automatic image registration (HAIR) algorithms. High performance is achieved by: 1) reduction in search data, 2) reduction in search space, and 3) parallel processing. Reduction in search data is achieved by performing registration using only subimages. A new metric called registrability is used to select those subimages such that accuracy is maintained. In addition, a histogram comparison is used to discard anomalous subimages, such as those with clouds. Further data reduction is obtained using an iterative refinement search (IRA), which exploits the wavelet multi-resolution representation. This technique starts searching images with lower resolution first, then refining the results using higher resolution images to use the least possible data points in the overall registration task. Reduction of search space is achieved through two methods. Firs...

strategies based image registration via feature matching

In automated fingerprint identification systems, an efficient and accurate alignment algorithm in the preprocessing stage plays a crucial role in the performance of the whole system. In this paper, we explore the use of genetic algorithms for optimizing the alignment of a pair of fingerprint images. To test its performance, we compare the implemented genetic algorithm with two other algorithms, namely, a 2D and 3D algorithms. Based upon our experiment on 250 pairs of fingerprint images, we find that: 1) genetic algorithms run ten times faster that 3D algorithm with similar alignment accuracy, and 2) genetic algorithms are 13 % more accurate than 2D algorithm, with same running time. The conclusion drawn from this study is that a genetic algorithm approach is an efficient and effective approach for fingerprint image registration. 1.

In remote sensing, image registration is to find the best transform between a reference and input images that may be different due to changes in position or altitude of or noise in the sensors. Image registration is one of the first steps in the analysis of remotely sensed images and requires high computational resources. The computation time is affected by two factors: search data size and search space. This paper describes an efficient image registration algorithm that uses multi-resolution wavelet decomposed images to reduce the search data size, and Genetic Algorithms to optimize the search solution space. Experimental results have shown subpixel accuracy and high efficiency over conventional methods. 1.