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GECCO
2008
Springer
172views Optimization» more  GECCO 2008»
15 years 5 months ago
Empirical analysis of a genetic algorithm-based stress test technique
Evolutionary testing denotes the use of evolutionary algorithms, e.g., Genetic Algorithms (GAs), to support various test automation tasks. Since evolutionary algorithms are heuris...
Vahid Garousi
GECCO
2008
Springer
141views Optimization» more  GECCO 2008»
15 years 5 months ago
Potential fitness for genetic programming
We introduce potential fitness, a variant of fitness function that operates in the space of schemata and is applicable to tree-based genetic programing. The proposed evaluation ...
Krzysztof Krawiec, PrzemysBaw Polewski
GECCO
2008
Springer
115views Optimization» more  GECCO 2008»
15 years 5 months ago
A genetic programming approach to business process mining
The aim of process mining is to identify and extract process patterns from data logs to reconstruct an overall process flowchart. As business processes become more and more comple...
Chris J. Turner, Ashutosh Tiwari, Jörn Mehnen
GECCO
2008
Springer
116views Optimization» more  GECCO 2008»
15 years 5 months ago
Stock trading strategies by genetic network programming with flag nodes
Genetic Network Programming (GNP) has been proposed as a graph-based evolutionary algorithm. GNP works well especially in dynamic environments due to its graph structures. In addi...
Shingo Mabu, Yan Chen, Etsushi Ohkawa, Kotaro Hira...
GECCO
2005
Springer
196views Optimization» more  GECCO 2005»
15 years 10 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