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GECCO
2008
Springer
117views Optimization» more  GECCO 2008»
15 years 7 months ago
Is "best-so-far" a good algorithmic performance metric?
In evolutionary computation, experimental results are commonly analyzed using an algorithmic performance metric called best-so-far. While best-so-far can be a useful metric, its u...
Nathaniel P. Troutman, Brent E. Eskridge, Dean F. ...
GECCO
2008
Springer
141views Optimization» more  GECCO 2008»
15 years 7 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 7 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
154views Optimization» more  GECCO 2008»
15 years 7 months ago
Cooperative network construction using digital germlines
This paper describes a study in the evolution of cooperative behavior, specifically the construction of communication networks, through digital evolution and multilevel selection...
David B. Knoester, Philip K. McKinley, Charles Ofr...
GECCO
2008
Springer
133views Optimization» more  GECCO 2008»
15 years 7 months ago
Using feature-based fitness evaluation in symbolic regression with added noise
Symbolic regression is a popular genetic programming (GP) application. Typically, the fitness function for this task is based on a sum-of-errors, involving the values of the depe...
Janine H. Imada, Brian J. Ross