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GECCO
2003
Springer
14 years 21 days ago
Enhancing the Performance of GP Using an Ancestry-Based Mate Selection Scheme
The performance of genetic programming relies mostly on population-contained variation. If the population diversity is low then there will be a greater chance of the algorithm bein...
Rodney Fry, Andrew M. Tyrrell
GECCO
2005
Springer
141views Optimization» more  GECCO 2005»
14 years 1 months ago
Evolving cooperative strategies for UAV teams
We present a Genetic Programming approach to evolve cooperative controllers for teams of UAVs. Our focus is a collaborative search mission in an uncertain and/or hostile environme...
Marc D. Richards, L. Darrell Whitley, J. Ross Beve...
GECCO
2006
Springer
166views Optimization» more  GECCO 2006»
13 years 11 months ago
Comparing genetic robustness in generational vs. steady state evolutionary algorithms
Previous research has shown that evolutionary systems not only try to develop solutions that satisfy a fitness requirement, but indirectly attempt to develop genetically robust so...
Josh Jones, Terry Soule
EUROGP
2009
Springer
119views Optimization» more  EUROGP 2009»
14 years 2 months ago
Comparison of CGP and Age-Layered CGP Performance in Image Operator Evolution
This paper analyses the efficiency of the Cartesian Genetic Programming (CGP) methodology in the image operator design problem at the functional level. The CGP algorithm is compare...
Karel Slaný
EUROGP
2009
Springer
138views Optimization» more  EUROGP 2009»
14 years 2 months ago
Self Modifying Cartesian Genetic Programming: Fibonacci, Squares, Regression and Summing
Self Modifying CGP (SMCGP) is a developmental form of Cartesian Genetic Programming(CGP). It is able to modify its own phenotype during execution of the evolved program. This is do...
Simon Harding, Julian Francis Miller, Wolfgang Ban...