%0 Conference Paper %1 miller:1999:ACGP %A Miller, Julian F. %B Proceedings of the Genetic and Evolutionary Computation Conference %C Orlando, Florida, USA %D 1999 %E Banzhaf, Wolfgang %E Daida, Jason %E Eiben, Agoston E. %E Garzon, Max H. %E Honavar, Vasant %E Jakiela, Mark %E Smith, Robert E. %I Morgan Kaufmann %K algorithms, and evolvable genetic hardware programming %P 1135--1142 %T An empirical study of the efficiency of learning boolean functions using a Cartesian Genetic Programming approach %U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GP-411.ps %V 2 %X 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. %@ 1-55860-611-4

@inproceedings{miller:1999:ACGP, 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.}, added-at = {2008-06-19T17:46:40.000+0200}, address = {Orlando, Florida, USA}, author = {Miller, Julian F.}, biburl = {https://www.bibsonomy.org/bibtex/26f47fbed01bbf2b00d85011699a5549e/brazovayeye}, booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference}, editor = {Banzhaf, Wolfgang and Daida, Jason and Eiben, Agoston E. and Garzon, Max H. and Honavar, Vasant and Jakiela, Mark and Smith, Robert E.}, interhash = {0f4032643fef94d8f47467097dbabc26}, intrahash = {6f47fbed01bbf2b00d85011699a5549e}, isbn = {1-55860-611-4}, keywords = {algorithms, and evolvable genetic hardware programming}, month = {13-17 July}, 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)}, pages = {1135--1142}, publisher = {Morgan Kaufmann}, publisher_address = {San Francisco, CA 94104, USA}, timestamp = {2008-06-19T17:47:13.000+0200}, title = {An empirical study of the efficiency of learning boolean functions using a Cartesian Genetic Programming approach}, url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GP-411.ps}, volume = 2, year = 1999 }