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GECCO
2005
Springer
136views Optimization» more  GECCO 2005»
9 years 4 months ago
On the stationary distribution of GAs with fixed crossover probability
We analyse the convergence of a GA when the mutation probability is low and the selection pressure is high, for arbitrary crossover types and probabilities. We succeed in mathemat...
U. Chandimal de Silva, Joe Suzuki
GECCO
2005
Springer
196views Optimization» more  GECCO 2005»
9 years 4 months ago
Breeding swarms: a new approach to recurrent neural network training
This paper shows that a novel hybrid algorithm, Breeding Swarms, performs equal to, or better than, Genetic Algorithms and Particle Swarm Optimizers when training recurrent neural...
Matthew Settles, Paul Nathan, Terence Soule
GECCO
2005
Springer
150views Optimization» more  GECCO 2005»
9 years 4 months ago
A GA for maximum likelihood phylogenetic inference using neighbour-joining as a genotype to phenotype mapping
Evolutionary relationships among species can be represented by a phylogenetic tree and inferred by optimising some measure of fitness, such as the statistical likelihood of the t...
Leon Poladian
GECCO
2005
Springer
170views Optimization» more  GECCO 2005»
9 years 4 months ago
Multiobjective shape optimization with constraints based on estimation distribution algorithms and correlated information
A new approach based on Estimation Distribution Algorithms for constrained multiobjective shape optimization is proposed in this article. Pareto dominance and feasibility rules ar...
Sergio Ivvan Valdez Peña, Salvador Botello ...
GECCO
2005
Springer
138views Optimization» more  GECCO 2005»
9 years 4 months ago
Artificial immune system for solving generalized geometric problems: a preliminary results
Generalized geometric programming (GGP) is an optimization method in which the objective function and constraints are nonconvex functions. Thus, a GGP problem includes multiple lo...
Jui-Yu Wu, Yun-Kung Chung
GECCO
2005
Springer
154views Optimization» more  GECCO 2005»
9 years 4 months ago
Genetic algorithms using low-discrepancy sequences
The random number generator is one of the important components of evolutionary algorithms (EAs). Therefore, when we try to solve function optimization problems using EAs, we must ...
Shuhei Kimura, Koki Matsumura
GECCO
2005
Springer
156views Optimization» more  GECCO 2005»
9 years 4 months ago
The blob code is competitive with edge-sets in genetic algorithms for the minimum routing cost spanning tree problem
Among the many codings of spanning trees for evolutionary search are those based on bijections between Pr¨ufer strings—strings of n−2 vertex labels—and spanning trees on th...
Bryant A. Julstrom
GECCO
2005
Springer
131views Optimization» more  GECCO 2005»
9 years 4 months ago
Statistical analysis of heuristics for evolving sorting networks
Designing efficient sorting networks has been a challenging combinatorial optimization problem since the early 1960’s. The application of evolutionary computing to this problem ...
Lee K. Graham, Hassan Masum, Franz Oppacher
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