Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Genetic programming II: automatic discovery of reusable programs
Genetic programming II: automatic discovery of reusable programs
Genetic programming: an introduction: on the automatic evolution of computer programs and its applications
Target detection in SAR imagery by genetic programming
Advances in Engineering Software
Genetic Programming for Feature Discovery and Image Discrimination
Proceedings of the 5th International Conference on Genetic Algorithms
Genetic Programming for Multiple Class Object Detection
AI '99 Proceedings of the 12th Australian Joint Conference on Artificial Intelligence: Advanced Topics in Artificial Intelligence
A domain-independentwindow approach to multiclass object detection using genetic programming
EURASIP Journal on Applied Signal Processing
Connection Science - Evolutionary Learning and Optimisation
Classification of seafloor habitats using genetic programming
Evo'08 Proceedings of the 2008 conference on Applications of evolutionary computing
CLONAL-GP framework for artificial immune system inspired genetic programming for classification
KES'10 Proceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems: Part I
A gaussian groundplan projection area model for evolving probabilistic classifiers
Proceedings of the 13th annual conference on Genetic and evolutionary computation
DepthLimited crossover in GP for classifier evolution
Computers in Human Behavior
Two-Tier genetic programming: towards raw pixel-based image classification
Expert Systems with Applications: An International Journal
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In this paper a new method is presented to solve a series of multiclass object classification problems using Genetic Programming (GP). All component two-class subproblems of the multiclass problem are solved in a single run, using a multi-objective fitness function. Probabilistic methods are used, with each evolved program required to solve only one subproblem. Programs gain a fitness related to their rank at the subproblem that they solve best. The new method is compared with two other GP based methods on four multiclass object classification problems of varying difficulty. The new method outperforms the other methods significantly in terms of both test classification accuracy and training time at the best validation performance in almost all experiments.