99 search results - page 6 / 20 » Evolving Crossover Operators for Function Optimization |
GECCO
2008
Springer
13 years 8 months ago
2008
Springer
We propose a new class of crossover operators for genetic algorithms (CrossNet) which use a network-based (or graphbased) chromosomal representation. We designed CrossNet with the...
GECCO
2000
Springer
13 years 11 months ago
2000
Springer
In this paper we describe a method for improving genetic-algorithm-based optimization using informed genetic operators. The idea is to make the genetic operators such as mutation ...
EC
2006
13 years 7 months ago
2006
Learning Classifier Systems (LCSs), such as the accuracy-based XCS, evolve distributed problem solutions represented by a population of rules. During evolution, features are speci...
GECCO
2007
Springer
14 years 1 months ago
2007
Springer
Recent work has provided functions that can be used to prove a principled distinction between the capabilities of mutation-based and crossover-based algorithms. However, prior fun...
GECCO
2006
Springer
13 years 11 months ago
2006
Springer
Evolutionary algorithms tend to produce solutions that are not evolvable: Although current fitness may be high, further search is impeded as the effects of mutation and crossover ...