2016 5th Brazilian Conference on Intelligent Systems (BRACIS) / Institute of Electrical and Electronics Engineers.

Format:

Book

Author/Creator:

Institute of Electrical and Electronics Engineers, author, issuing body.

Language:

English

Subjects (All):

Artificial intelligence--Congresses.

Artificial intelligence.

Physical Description:

1 online resource

Other Title:

2016 5th Brazilian Conference on Intelligent Systems

Place of Publication:

Piscataway, New Jersey : IEEE, 2016.

Summary:

Sequential Symbolic Regression (SSR) is a technique that recursively induces functions over the error of the current solution, concatenating them in an attempt to reduce the error of the resulting model. As proof of concept, the method was previously evaluated in one-dimensional problems and compared with canonical Genetic Programming (GP) and Geometric Semantic Genetic Programming (GSGP). In this paper we revisit SSR exploring the method behaviour in higher dimensional, larger and more heterogeneous datasets. We discuss the difficulties arising from the application of the method to more complex problems, e.g., overfitting, along with suggestions to overcome them. An experimental analysis was conducted comparing SSR to GP and GSGP, showing SSR solutions are smaller than those generated by the GSGP with similar performance and more accurate than those generated by the canonical GP.

Notes:

Description based on publisher supplied metadata and other sources.

ISBN:

9781509035663

1509035664