Rule-based Genetic Programming

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 dras-tically and hence, the genetic reproduction operations are much more likely to produce good results than in other Ge-netic 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 emer-gent algorithms for mutual exclusion at a distributed critical section