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by Christian Igel, Martin Kreutz
http://www.neuroinformatik.ruhr-uni-bochum.de/ini/PEOPLE/igel/UFDtItEoLS.ps.gz
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Abstract:
Abstract- In this paper, the absolute benefit, a measure of improvement in the fitness space, is derived from the viewpoint of fitness distribution and fitness trajectory analysis. It is used for online operator-adaptation, where the optimization of density estimation models serves as an example. A new information theory based measure is proposed to judge the accuracy of the evolved models. Further, the absolute benefit is applied to offline analysis of new gradient based operators used for coefficient adaptation in genetic programming. An efficient method to calculate the gradient information is presented. 1
Citations
|
4363
|
Elements of Information Theory
– Cover, Thomas
- 1991
|
|
3051
|
Neural Networks for Pattern Recognition
– Bishop
- 1995
|
|
1782
|
Genetic Programming: On the Programming of Computers by Means of Natural Selection Cambridge
– Koza
- 1992
|
|
593
|
Hierarchical Mixtures of Experts and the EM algorithm
– Jordan, Jacobs
- 1993
|
|
524
|
Networks for approximation and learning
– Poggio, Girosi
- 1990
|
|
489
|
Neural networks and the bias/variance dilemma
– Geman, Bienenstock, et al.
- 1992
|
|
384
|
Evolution and Optimum Seeking
– Schwefel
- 1995
|
|
374
|
Mixture densities, maximum likelihood and the em algorithm
– Redner, Walker
- 1984
|
|
271
|
Optimization of Control Parameters for Genetic Algorithms
– Grefenstette
- 1986
|
|
148
|
Adapting operator probabilities in genetic algorithm
– Davis
- 1989
|
|
141
|
The evolution of evolvability in genetic programming
– Altenberg
- 1994
|
|
59
|
Evolutionsstrategie '94
– Rechenberg
- 1994
|
|
50
|
Subtree crossover: Building block engine or macromutation
– Angeline
- 1997
|
|
31
|
Operator and parameter adaptation in genetic algorithms
– Smith, Fogarty
- 1997
|
|
25
|
Oppacher: “Program Search with a Hierarchical Variable Length Representation
– O'Reilly, Franz
- 1994
|
|
23
|
Evolutionary programming with tree mutations: Evolving computer programs without crossover
– Chellapilla
- 1997
|
|
23
|
Evolutionary induction of sparse neural trees
– Zhang, Ohm, et al.
- 1997
|
|
19
|
P.: Adapting operator settings in genetic algorithms
– Tuson, Ross
- 1998
|
|
16
|
Two scientific applications of genetic programming: Stack filters and non-linear equation fitting to chaotic data
– Oakley
- 1994
|
|
15
|
Using fitness distributions to design more efficient evolutionary computations
– Fogel, Ghozeil
- 1996
|
|
15
|
Predictive models using fitness distributions of genetic operators
– Grefenstette
- 1995
|
|
7
|
Factory Car Audio Repair For All Makes and Models
– Sendhoff, Kreutz, et al.
- 1997
|
|
5
|
Evolving predictors for chaotic time series
– Angeline
- 1998
|
|
2
|
Fitness distributions: Tools for designing efficient evolutionary computations
– Igel, Chellapilla
- 1999
|
|
2
|
von Seelen. Optimisation of density estimation models with evolutionary algorithms
– Kreutz, Reimetz, et al.
- 1998
|
|
2
|
Seelen. Structure optimization of density estimation models applied to regression problems with dynamic noise
– Kreutz, Reimetz, et al.
- 1999
|
|
1
|
Tackling the "curse of dimensionality " of radial basis function neural networks using a genetic algorithm
– Carse, Fogarty
- 1996
|
|
1
|
Penalized likelihood. Encyclopaedia of Statistical Sciences, update volume
– Green
- 1996
|