Distributed Hybrid Genetic Programming for Learning Boolean Functions

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{DrostePPSN2000,

• author = "Stefan Droste and Dominic Heutelbeck and Ingo Wegener",
• title = "Distributed Hybrid Genetic Programming for Learning {Boolean} Functions",
• booktitle = "Parallel Problem Solving from Nature - PPSN VI 6th International Conference",
• editor = "Marc Schoenauer and Kalyanmoy Deb and G{\"u}nter Rudolph and Xin Yao and Evelyne Lutton and Juan Julian Merelo and Hans-Paul Schwefel",
• year = "2000",
• publisher = "Springer Verlag",
• address = "Paris, France",
• month = "16-20 " # sep,
• volume = "1917",
• series = "LNCS",
• pages = "181--190",
• keywords = "genetic algorithms, genetic programming",
• URL = "http://ls2-www.cs.uni-dortmund.de/~wegener/papers/Paper93.ps",
• URL = "http://eldorado.uni-dortmund.de/0x81d98002_0x00034a39",
• URL = "http://citeseer.ist.psu.edu/322232.html",
• abstract = "When genetic programming (GP) is used to find programs with Boolean inputs and outputs, ordered binary decision diagrams (OBDDs) are often used successfully. In all known OBDD-based GP-systems the variable ordering, a crucial factor for the size of OBDDs, is preset to an optimal ordering of the known test function. Certainly this cannot be done in practical applications, where the function to learn and hence its optimal variable ordering are unknown. Here, the first GP-system is presented that evolves the variable ordering of the OBDDs and the OBDDs itself by using a distributed hybrid approach. For the experiments presented the unavoidable size increase compared to the optimal variable ordering is quite small. Hence, this approach is a big step towards learning well-generalizing Boolean functions",

}

Genetic Programming entries for Stefan Droste Dominic Heutelbeck Ingo Wegener

Citations