Polar IFS+Parisian Genetic Programming=Efficient IFS Inverse Problem Solving
Genetic Programming and Evolvable Machines
Dynamic flies: a new pattern recognition tool applied to stereo sequence processing
Pattern Recognition Letters
Comparing Viewpoint Evaluation Functions for Model-Based Inspectional Coverage
CRV '04 Proceedings of the 1st Canadian Conference on Computer and Robot Vision
3DPVT '04 Proceedings of the 3D Data Processing, Visualization, and Transmission, 2nd International Symposium
Evolutionary computation for sensor planning: the task distribution plan
EURASIP Journal on Applied Signal Processing
Parisian camera placement for vision metrology
Pattern Recognition Letters - Special issue: Evolutionary computer vision and image understanding
Combatting financial fraud: a coevolutionary anomaly detection approach
Proceedings of the 10th annual conference on Genetic and evolutionary computation
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We present a novel camera network design methodology based on the Parisian approach to evolutionary computation. The problem is partitioned into a set of homogeneous elements, whose individual contribution to the problem solution can be evaluated separately. These elements are allocated in a population with the goal of creating a single solution by a process of aggregation. Thus, the goal of the evolutionary process is to generate individuals that jointly form better solutions. Under the proposed paradigm, aspects such as problem decomposition and representation, as well as local and global fitness integration need to be addressed. Experimental results illustrate significant improvements, in terms of solution quality and computational cost, when compared to canonical evolutionary approaches.