A connectionist machine for genetic hillclimbing
A connectionist machine for genetic hillclimbing
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Experiments on multistrategy learning by meta-learning
CIKM '93 Proceedings of the second international conference on Information and knowledge management
Using Genetic Algorithms for Concept Learning
Machine Learning - Special issue on genetic algorithms
Genetic programming II (videotape): the next generation
Genetic programming II (videotape): the next generation
Elements of machine learning
An introduction to genetic algorithms
An introduction to genetic algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Machine Learning
Digital Image Processing
Automatic Creation of Human-Competitive Programs and Controllers by Means of Genetic Programming
Genetic Programming and Evolvable Machines
Experiments on Solving Multiclass Learning Problems by n2-classifier
ECML '98 Proceedings of the 10th European Conference on Machine Learning
A Contolled Experiment: Evolution for Learning Difficult Image Classification
EPIA '95 Proceedings of the 7th Portuguese Conference on Artificial Intelligence: Progress in Artificial Intelligence
Pairwise Comparison of Hypotheses in Evolutionary Learning
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
A new modular genetic programming for finding attractive technical patterns in stock markets
Proceedings of the 12th annual conference on Genetic and evolutionary computation
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This paper investigates the use of evolutionary programming for the search of hypothesis space in visual learning tasks. The general goal of the project is to elaborate human-competitive procedures for pattern discrimination by means of learning based on the training data (set of images). In particular, the topic addressed here is the comparison between the 'standard' genetic programming (as defined by Koza [13]) and the genetic programming extended by local optimization of solutions, so-called genetic local search. The hypothesis formulated in the paper is that genetic local search provides better solutions (i.e. classifiers with higher predictive accuracy) than the genetic search without that extension. This supposition was positively verified in an extensive comparative experiment of visual learning concerning the recognition of handwritten characters.