Abstract:
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.
Citations
|
3356
|
C4.5: Programs for Machine Learning
– Quinlan
- 1993
|
|
1170
|
Genetic algorithms in search, optimization, and machine learning
– Goldberg
- 1989
|
|
919
|
M: An introduction to genetic algorithms The
– Mitchell
- 1997
|
|
660
|
An analysis of the Behavior of a Class of Genetic Adaptive Systems
– Jong
- 1975
|
|
592
|
Wrappers for feature subset selection
– Kohavi, John
- 1996
|
|
550
|
Adaptation in Natural and Artificial Systems. Ann Arbor: The University of Michigan Press
– Holland
- 1975
|
|
265
|
The discipline of machine learning
– Mitchell
- 2006
|
|
217
|
Digital Image Processing
– Gonzalez, Woods
- 1992
|
|
191
|
A Connectionist Machine for Genetic Hillclimbing
– Ackley
- 1987
|
|
116
|
Feature subset selection using a genetic algorithm. In: Liu Huan and Motoda Hiroshi Eds., Feature Extraction, Construction and Selection: A Data Mining Perspective
– Yang, Honovar
- 1998
|
|
101
|
Using genetic algorithms for concept learning
– Jong, Spears, et al.
- 1992
|
|
99
|
Elements of Machine Learning
– Langley
- 1996
|
|
64
|
Experiments on multistrategy learning by meta-learning
– Chan, Stolfo
- 1993
|
|
57
|
Multicriteria decision-aid
– Vincke
- 1992
|
|
40
|
Evolving visual routines
– Johnson, Maes, et al.
- 1994
|
|
40
|
Genetic Programming for Image Analysis
– Poli
- 1996
|
|
22
|
Feature selection methods: Genetic algorithms vs. greedy-like search
– Vafaie, Imam
- 1994
|
|
20
|
Genetic Programming 2
– Koza
- 1994
|
|
17
|
A controlled experiment: Evolution for learning difficult image classification
– Teller, Veloso
- 1995
|
|
15
|
On the performance of multiple objective genetic local search on the 0/1 knapsack problem. a comparative experiment
– Jaszkiewicz
- 2000
|
|
14
|
Do not worry, be messy
– Goldberg, Deb, et al.
- 1991
|
|
6
|
Stefanowski J: Experiments on solving multiclass learning problems by n 2 -classifier
– Jelonek, Stefanowski
- 1998
|
|
6
|
Pairwise Comparison of Hypotheses in Evolutionary Learning
– Krawiec
- 2001
|
|
6
|
et al., Comparison of learning algorithms for handwritten digit recognition
– LeCun
- 1995
|
|
5
|
Multistrategy learning from engineering data by integrating inductive generalization and genetic algorithms
– Bala, Pachowicz, et al.
- 1994
|
|
5
|
Constructive Induction in Picture-based Decision Support
– Krawiec
- 2000
|
|
4
|
Evolutionary weighting of image features for diagnosing of CNS tumors
– Komosinski, Krawiec
|
|
3
|
Automatic Creation of Human--Competetive Programs and Controllers by
– Koza, Keane, et al.
- 2000
|
|
3
|
Genetic Programming with Local Improvement for Visual Learning from Examples
– Krawiec
- 2001
|
|
1
|
Pairwise Comparison of Hypotheses Coverings as a Natural Mean Agains Undesirable Niching in Evolutionary Inductive Learning
– Krawiec
- 2001
|
