2003/09/09 by C. Huang, Chien‐Feng Huang, Huang, C. +3
Biochemistry, Genetics and Molecular Biology · Computer Science · Physics and Astronomy · #Adaptation and Self-Organizing Systems (nlin.AO) #Artificial Intelligence (cs.AI) #FOS: Biological sciences #FOS: Computer and information sciences #FOS: Physical sciences #Genomics (q-bio.GN) #I.2 #Neural and Evolutionary Computing (cs.NE) #RNA Research and Splicing #RNA regulation and disease #cs.AI #cs.NE #nlin.AO #q-bio.GN
paper · pdf · doi:10.48550/arxiv.cs/0309012
Proceedings of the 2003 IEEE Congress on Evolutionary Computation. Camberra, Australia, December 2003
arxiv created 2003/09/09 · openalex publication_date 2003/09/09 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This paper presents our computational methodology using Genetic Algorithms (GA) for exploring the nature of RNA editing. These models are constructed using several genetic editing characteristics that are gleaned from the RNA editing system as observed in several organisms. We have expanded the traditional Genetic Algorithm with artificial editing mechanisms as proposed by (Rocha, 1997). The incorporation of editing mechanisms provides a means for artificial agents with genetic descriptions to gain greater phenotypic plasticity, which may be environmentally regulated. Our first implementations of these ideas have shed some light into the evolutionary implications of RNA editing. Based on these understandings, we demonstrate how to select proper RNA editors for designing more robust GAs, and the results will show promising applications to real-world problems. We expect that the framework proposed will both facilitate determining the evolutionary role of RNA editing in biology, and advance the current state of research in Genetic Algorithms.