%0 Conference Paper %1 WZG2007DGPFi %A Weise, Thomas %A Zapf, Michael %A Geihs, Kurt %B Proceedings of BIONETICS 2007, 2nd International Conference on Bio-Inspired Models of Network, Information, and Computing Systems %C Radisson SAS Beke Hotel, 43. Terez krt., Budapest H-1067, Hungary %D 2007 %I Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering (ICST), IEEE, ACM %K Classifier Critical Distributed Epistasis, GP, Genetic Learning Neutrality, Programming, Rule-based Section, Systems Systems, algorithms, genetic programming, %T Rule-based Genetic Programming %U http://www.it-weise.de/documents/files/WZG2007RBGP.pdf %X In this paper we introduce a new approach for Genetic Programming, called rule-based Genetic Programming, or RBGP in short. A program evolved in the RBGP syntax is a list of rules. Each rule consists of two conditions, combined with a logical operator, and an action part. Such rules are independent from each other in terms of position (mostly) and cardinality (always). This reduces the epistasis drastically and hence, the genetic reproduction operations are much more likely to produce good results than in other Genetic Programming methodologies. In order to verify the utility of our idea, we apply RBGP to a hard problem in distributed systems. With it, we are able to obtain emergent algorithms for mutual exclusion at a distributed critical section.

@inproceedings{WZG2007DGPFi, abstract = {In this paper we introduce a new approach for Genetic Programming, called rule-based Genetic Programming, or RBGP in short. A program evolved in the RBGP syntax is a list of rules. Each rule consists of two conditions, combined with a logical operator, and an action part. Such rules are independent from each other in terms of position (mostly) and cardinality (always). This reduces the epistasis drastically and hence, the genetic reproduction operations are much more likely to produce good results than in other Genetic Programming methodologies. In order to verify the utility of our idea, we apply RBGP to a hard problem in distributed systems. With it, we are able to obtain emergent algorithms for mutual exclusion at a distributed critical section.}, added-at = {2008-06-19T17:35:00.000+0200}, address = {Radisson SAS Beke Hotel, 43. Terez krt., Budapest H-1067, Hungary}, affiliation = {University of Kassel}, author = {Weise, Thomas and Zapf, Michael and Geihs, Kurt}, biburl = {https://www.bibsonomy.org/bibtex/2036d5a977fbc3438d13384d5c466403e/brazovayeye}, booktitle = {Proceedings of BIONETICS 2007, 2nd International Conference on Bio-Inspired Models of Network, Information, and Computing Systems}, interhash = {04f6987129bdde7ebf43a5f1469adbd0}, intrahash = {036d5a977fbc3438d13384d5c466403e}, isbn13 = {978-963-9799-05-9}, keywords = {Classifier Critical Distributed Epistasis, GP, Genetic Learning Neutrality, Programming, Rule-based Section, Systems Systems, algorithms, genetic programming,}, language = {en}, month = {December~10}, publisher = {Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering (ICST), IEEE, ACM}, timestamp = {2008-06-19T17:54:00.000+0200}, title = {Rule-based Genetic Programming}, url = {http://www.it-weise.de/documents/files/WZG2007RBGP.pdf}, year = 2007 }