On the Role of Non-effective Code in Linear Genetic Programming

Author(s)

Dal Piccol Sotto, Leo Francoso ; Rothlauf, Franz ; LopezIbanez, M

• Document type: Journal article
• Source: PROCEEDINGS OF THE 2019 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE (GECCO'19); v. N/A, p. 9-pg., 2019-01-01.

Abstract

In linear variants of Genetic Programming (GP) like linear genetic programming (LGP), structural introns can emerge, which are nodes that are not connected to the final output and do not contribute to the output of a program. There are claims that such non-effective code is beneficial for search, as it can store relevant and important evolved information that can be reactivated in later search phases. Furthermore, introns can increase diversity, which leads to higher GP performance. This paper studies the role of non-effective code by comparing the performance of LGP variants that deal differently with non-effective code for standard symbolic regression problems. As we find no decrease in performance when removing or randomizing structural introns in each generation of a LGP run, we have to reject the hypothesis that structural introns increase LGP performance by preserving meaningful sub-structures. Our results indicate that there is no important information stored in structural introns. In contrast, we find evidence that the increase of diversity due to structural introns positively affects LGP performance. (AU)

• FAPESP's process: 18/13202-4 - Development of a linear genetic programming algorithm using an estimation of distribution algorithm applied to supervised machine learning
• Grantee: Léo Françoso Dal Piccol Sotto
• Support Opportunities: Scholarships abroad - Research Internship - Doctorate (Direct)