This paper is the second part of a two-part paper which introduces a general schema theory for genetic programming (GP) with subtree-swapping crossover (Part I (Poli and McPhee, 2003)). Like other recent GP schema theory results, the theory gives an exact formulation (rather than a lower bound) for the expected number of instances of a schema at the next generation. The theory is based on a Car...

It is well-known that there exist consistent first-order theories that become inconsistent when we add Tarski’s schema T. This is Tarski’s Theorem. To avoid the inconsistency result, one can restrict Tarski’s schema in different ways. In our paper we restrict Tarski’s schema T by only instantiating the schema with a proper subset of the set of all sentences. We prove several results concerning ...

schema matching is a critical step in many applications, such as data warehouse loading, online analytical process (olap), data mining, semantic web [2] and schema integration. this task is defined for finding the semantic correspondences between elements of two schemas. recently, schema matching has found considerable interest in both research and practice. in this paper, we present a new impr...

Schemata and the schema theorem, although criticised, are often used to explain why genetic algorithms (GAs) work. A considerable research eeort has been produced recently to extend the GA schema theory to Genetic Programming (GP). In this paper we review the main results available to date in the theory of schemata for GP and some recent experimental work on schemata.

In English learning, good reading ability is very important. recent years, the scores of students' in college entrance examination have also increased. However, many cases, even though students can understand vocabulary and syntax text, they still cannot correctly grasp main point text. They are easily immersed details text key content. There a certain relationship between schema theory. The mo...

Schemata and the schema theorem, although criticised, are often used to explain why genetic algorithms (GAs) work. A considerable research eeort has been produced recently to extend the GA schema theory to Genetic Programming (GP). In this paper we review the main results available to date in the theory of schemata for GP and some recent experimental work on schemata.