Coevolutionary bid-based genetic programming for problem decomposition in classification

@article{Lichodzijewski2008CoevolutionaryBG,
  title={Coevolutionary bid-based genetic programming for problem decomposition in classification},
  author={Peter Lichodzijewski and Malcolm I. Heywood},
  journal={Genetic Programming and Evolvable Machines},
  year={2008},
  volume={9},
  pages={331-365}
}
In this work a cooperative, bid-based, model for problem decomposition is proposed with application to discrete action domains such as classification. This represents a significant departure from models where each individual constructs a direct input-outcome map, for example, from the set of exemplars to the set of class labels as is typical under the classification domain. In contrast, the proposed model focuses on learning a bidding strategy based on the exemplar feature vectors; each… CONTINUE READING
BETA

Similar Papers

Citations

Publications citing this paper.
SHOWING 1-10 OF 20 CITATIONS

Emergent Solutions to High-Dimensional Multitask Reinforcement Learning

  • Evolutionary Computation
  • 2018
VIEW 11 EXCERPTS
CITES METHODS & BACKGROUND
HIGHLY INFLUENCED

Analyzing string format-based classifiers for botnet detection: GP and SVM

  • 2013 IEEE Congress on Evolutionary Computation
  • 2013
VIEW 1 EXCERPT
CITES METHODS

References

Publications referenced by this paper.
SHOWING 1-10 OF 53 REFERENCES

For Real! XCS with Continuous-Valued Inputs

  • Evolutionary Computation
  • 2003
VIEW 4 EXCERPTS
HIGHLY INFLUENTIAL

An algorithmic description of XCS

VIEW 4 EXCERPTS
HIGHLY INFLUENTIAL

Classifier Fitness Based on Accuracy

  • Evolutionary Computation
  • 1994
VIEW 6 EXCERPTS
HIGHLY INFLUENTIAL

Cognitive systems based on adaptive algorithms

  • SIGART Newsletter
  • 1977
VIEW 5 EXCERPTS
HIGHLY INFLUENTIAL

Evolving Teams of Predictors with Linear Genetic Programming

  • Genetic Programming and Evolvable Machines
  • 2001
VIEW 4 EXCERPTS
HIGHLY INFLUENTIAL