Sciweavers

GECCO
2005
Springer
131views Optimization» more  GECCO 2005»
13 years 11 months ago
EA models and population fixed-points versus mutation rates for functions of unitation
Using a dynamic systems model for the Simple Genetic Algorithm due to Vose[1], we analyze the fixed point behavior of the model without crossover applied to functions of unitation...
J. Neal Richter, John Paxton, Alden H. Wright
GECCO
2005
Springer
153views Optimization» more  GECCO 2005»
13 years 11 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 11 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 11 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
288views Optimization» more  GECCO 2005»
13 years 11 months ago
A comparison study between genetic algorithms and bayesian optimize algorithms by novel indices
Genetic Algorithms (GAs) are a search and optimization technique based on the mechanism of evolution. Recently, another sort of population-based optimization method called Estimat...
Naoki Mori, Masayuki Takeda, Keinosuke Matsumoto
GECCO
2005
Springer
177views Optimization» more  GECCO 2005»
13 years 11 months ago
Evolving next generation signal compression and reconstruction transforms via genetic algorithms
Ongoing research has established a new methodology for using genetic algorithms [2] to evolve forward and inverse transforms that significantly reduce quantization error in recons...
Frank W. Moore, Patrick Marshall
GECCO
2005
Springer
136views Optimization» more  GECCO 2005»
13 years 11 months ago
A genetic algorithm for optimized reconstruction of quantized one-dimensional signals
This paper describes a genetic algorithm (GA) that evolves optimized sets of coefficients for one-dimensional signal reconstruction under lossy conditions due to quantization. Beg...
Frank W. Moore
GECCO
2005
Springer
13 years 11 months ago
New topologies for genetic search space
We propose three distance measures for genetic search space. One is a distance measure in the population space that is useful for understanding the working mechanism of genetic al...
Yong-Hyuk Kim, Byung Ro Moon
GECCO
2005
Springer
125views Optimization» more  GECCO 2005»
13 years 11 months ago
Schema disruption in tree-structured chromosomes
We study if and when the inequality dp(H) ≤ rel∆(H) holds for schemas H in chromosomes that are structured as trees. The disruption probability dp(H) is the probability that a...
William A. Greene
GECCO
2005
Springer
131views Optimization» more  GECCO 2005»
13 years 11 months ago
Genetic algorithms for the sailor assignment problem
This paper examines a real-world application of genetic algorithms – solving the United States Navy’s Sailor Assignment Problem (SAP). The SAP is a complex assignment problem ...
Deon Garrett, Joseph Vannucci, Rodrigo Silva, Dipa...