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GECCO
2005
Springer
127views Optimization» more  GECCO 2005»
13 years 10 months ago
Evolutionary form-finding of tensegrity structures
Tensegrity structures are stable 3-dimensional mechanical structures which maintain their form due to an intricate balance of forces between disjoint rigid elements and continuous...
Chandana Paul, Hod Lipson, Francisco J. Valero Cue...
GECCO
2005
Springer
156views Optimization» more  GECCO 2005»
13 years 10 months ago
Extraction of informative genes from microarray data
Identification of those genes that might anticipate the clinical behavior of different types of cancers is challenging due to availability of a smaller number of patient samples...
Topon Kumar Paul, Hitoshi Iba
GECCO
2005
Springer
200views Optimization» more  GECCO 2005»
13 years 10 months ago
An extension of vose's markov chain model for genetic algorithms
The paper presents an extension of Vose’s Markov chain model for genetic algorithm (GA). The model contains not only standard genetic operators such as mutation and crossover bu...
Anna Paszynska
GECCO
2005
Springer
160views Optimization» more  GECCO 2005»
13 years 10 months ago
Exploring relationships between genotype and oral cancer development through XCS
In medical research, being able to justify decisions is generally as important as taking the right ones. Interpretability is then one of the chief characteristics a learning algor...
Alessandro Passaro, Flavio Baronti, Valentina Magg...
GECCO
2005
Springer
175views Optimization» more  GECCO 2005»
13 years 10 months ago
Evolution of multi-loop controllers for fixed morphology with a cyclic genetic algorithm
Cyclic genetic algorithms can be used to generate single loop control programs for robots. While successful in generating controllers for individual leg movement, gait generation,...
Gary B. Parker, Ramona Georgescu
GECCO
2005
Springer
153views Optimization» more  GECCO 2005»
13 years 10 months ago
Evolving neural network ensembles for control problems
In neuroevolution, a genetic algorithm is used to evolve a neural network to perform a particular task. The standard approach is to evolve a population over a number of generation...
David Pardoe, Michael S. Ryoo, Risto Miikkulainen
GECCO
2005
Springer
232views Optimization» more  GECCO 2005»
13 years 10 months ago
A hardware pipeline for function optimization using genetic algorithms
Genetic Algorithms (GAs) are very commonly used as function optimizers, basically due to their search capability. A number of different serial and parallel versions of GA exist. ...
Malay Kumar Pakhira, Rajat K. De
GECCO
2005
Springer
116views Optimization» more  GECCO 2005»
13 years 10 months ago
Terrain generation using genetic algorithms
We propose a method for applying genetic algorithms to create 3D terrain data sets. Existing procedural algorithms for generation of terrain have several shortcomings. The most po...
TeongJoo Ong, Ryan Saunders, John Keyser, John J. ...
GECCO
2005
Springer
13 years 10 months ago
Scalability of genetic programming and probabilistic incremental program evolution
Radovan Ondas, Martin Pelikan, Kumara Sastry
GECCO
2005
Springer
129views Optimization» more  GECCO 2005»
13 years 10 months ago
Evolutionary change in developmental timing
This paper presents a mutation-based evolutionary algorithm that evolves genotypic genes for regulating developmental timing of phenotypic values. The genotype sequentially genera...
Kei Ohnishi, Kaori Yoshida