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GECCO
2005
Springer
15 years 10 months ago
Function choice, resiliency and growth in genetic programming
In this paper we examine how the choice of functions in a genetic program (GP) affects the rate of code growth and the development of resilient individuals. We find that functio...
Sireesha Besetti, Terence Soule
EUROGP
2001
Springer
105views Optimization» more  EUROGP 2001»
15 years 9 months ago
Adaptive Genetic Programming Applied to New and Existing Simple Regression Problems
Abstract. In this paper we continue our study on adaptive genetic programming. We use Stepwise Adaptation of Weights (saw) to boost performance of a genetic programming algorithm o...
Jeroen Eggermont, Jano I. van Hemert
ERCIMDL
2000
Springer
147views Education» more  ERCIMDL 2000»
15 years 8 months ago
Map Segmentation by Colour Cube Genetic K-Mean Clustering
Segmentation of a colour image composed of different kinds of texture regions can be a hard problem, namely to compute for an exact texture fields and a decision of the optimum num...
Vitorino Ramos, Fernando Muge
MONET
2008
85views more  MONET 2008»
15 years 4 months ago
Population Adaptation for Genetic Algorithm-based Cognitive Radios
Genetic algorithms are best suited for optimization problems involving large search spaces. The problem space encountered when optimizing the transmission parameters of an agile or...
Timothy R. Newman, Rakesh Rajbanshi, Alexander M. ...
AIPRF
2008
15 years 6 months ago
A Coherent and Heterogeneous Approach to Clustering
Despite outstanding successes of the state-of-the-art clustering algorithms, many of them still suffer from shortcomings. Mainly, these algorithms do not capture coherency and homo...
Arian Maleki, Nima Asgharbeygi