GENITOR II.: a distributed genetic algorithm
Journal of Experimental & Theoretical Artificial Intelligence
A Method for Registration of 3-D Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
IEEE Transactions on Pattern Analysis and Machine Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Introduction to the Theory of Neural Computation
Introduction to the Theory of Neural Computation
Genetic Algorithms for Placing Actuators on Space Structures
Proceedings of the 5th International Conference on Genetic Algorithms
A Genetic Algorithm for the Set Partitioning Problem
Proceedings of the 5th International Conference on Genetic Algorithms
Serial and Parallel Genetic Algorithms as Function Optimizers
Proceedings of the 5th International Conference on Genetic Algorithms
A New Approach on the Traveling Salesman Problem by Genetic Algorithms
Proceedings of the 5th International Conference on Genetic Algorithms
Proceedings of the 5th International Conference on Genetic Algorithms
Fitness Distance Correlation as a Measure of Problem Difficulty for Genetic Algorithms
Proceedings of the 6th International Conference on Genetic Algorithms
Removing the Genetics from the Standard Genetic Algorithm
Removing the Genetics from the Standard Genetic Algorithm
Fast and accurate shape-based registration
Fast and accurate shape-based registration
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Object localization has applications in many areas of engineeringand science. The goal is to spatially locate an arbitrarily shaped object.In many applications, it is desirable to minimize the number of measurementscollected while ensuring sufficient localization accuracy. In surgery, forexample, collecting a large number of localization measurements may eitherextend the time required to perform a surgical procedure or increase theradiation dosage to which a patient is exposed.Localization accuracy is a function of the spatial distribution ofdiscrete measurements over an object when measurement noise is present. Inprevious work (J. of Image Guided Surgery, Simon et al., 1995), metrics werepresented to evaluate the information available from a set of discreteobject measurements. In this study, new approaches to the discrete pointdata selection problem are described. These include hillclimbing, geneticalgorithms (GAs), and Population-Based Incremental Learning (PBIL).Extensions of the standard GA and PBIL methods that employ multipleparallel populations are explored. The results of extensive empiricaltesting are provided. The results suggest that a combination of PBIL andhillclimbing result in the best overall performance. A computer-assistedsurgical system that incorporates some of the methods presented in thispaper is currently being evaluated in cadaver trials.