An empirical study of the efficiency of learning Boolean functions using a Cartesian Genetic Programming approach

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

@InProceedings{miller:1999:ACGP,

• author = "Julian F. Miller",
• title = "An empirical study of the efficiency of learning {Boolean} functions using a Cartesian Genetic Programming approach",
• booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference",
• year = "1999",
• editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith",
• volume = "2",
• pages = "1135--1142",
• address = "Orlando, Florida, USA",
• publisher_address = "San Francisco, CA 94104, USA",
• month = "13-17 " # jul,
• publisher = "Morgan Kaufmann",
• keywords = "genetic algorithms, genetic programming, cartesian genetic programming, evolvable hardware",
• ISBN = "1-55860-611-4",
• URL = "http://citeseer.ist.psu.edu/153431.html",
• URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GP-411.ps",
• URL = "https://dl.acm.org/doi/10.5555/2934046.2934074",
• abstract = "A new form of Genetic Programming (GP) called Cartesian Genetic Programming (CGP) is proposed in which programs are represented by linear integer chromosomes in the form of connections and functionalities of a rectangular array of primitive functions. The effectiveness of this approach is investigated for boolean even-parity functions (3,4,5), and the 2-bit multiplier. The minimum number of evaluations required to give a 0.99 probability of evolving a target function is used to measure the efficiency of the new approach. It is found that extremely low populations are most effective. A simple probabilistic hillclimber (PH) is devised which proves to be even more effective. For these boolean functions either method appears to be much more efficient than the GP and Evolutionary Programming (EP) methods reported. The efficacy of the PH suggests that boolean function learning may not be an appropriate problem for testing the effectiveness of GP and EP.",
• notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)",

}

Genetic Programming entries for Julian F Miller

Citations