Loading...
Please wait, while we are loading the content...
Similar Documents
Theory-Laden Design of Mutation-Based Geometric Semantic Genetic Programming for Learning Classification Trees
| Content Provider | CiteSeerX |
|---|---|
| Author | Mambrini, Andrea Manzoni, Luca Moraglio, Alberto |
| Abstract | is a recently introduced form of Genetic Programming (GP), rooted in a geometric theory of representations, that searches the semantic space of functions. The fitness landscape seen by GSGP is always – for any domain and for any problem – unimodal with a linear slope by construction. This makes the search for the optimum much easier than for traditional GP, and it opens the way to analyse theoretically in a easy manner the optimisation time of GSGP in a general setting. Very recent work proposed a runtime analysis of mutation-based GSGP on the class of all Boolean function learning problems. We present a runtime analysis of mutation-based GSGP on the class of all classification tree learning problems, which is a classical application domain of standard GP. I. |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Learning Classification Tree Theory-laden Design Mutation-based Geometric Semantic Genetic Programming Mutation-based Gsgp Runtime Analysis Genetic Programming Easy Manner General Setting Traditional Gp Linear Slope Standard Gp Boolean Function Semantic Space Fitness Landscape Optimisation Time Classical Application Domain Geometric Theory Problem Unimodal Recent Work |
| Content Type | Text |
