A Comparison of Genetic Regulatory Network Dynamics and Encoding

被引：3

作者：

Disset, Jean ^{[1
]}

Wilson, Dennis G. ^{[1
]}

Cussat-Blanc, Sylvain ^{[1
]}

Sanchez, Stephane ^{[1
]}

Luga, Herve ^{[1
]}

Duthen, Yves ^{[1
]}

机构：

[1] Univ Toulouse, CNRS, UMR5505, IRIT, Toulouse 31015, France

来源：

PROCEEDINGS OF THE 2017 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE (GECCO'17) | 2017年

关键词：

Genetic Algorithms; fitness evaluations; representations; EVOLUTION;

D O I：

10.1145/3071178.3071322

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Genetic Regulatory Networks (GRNs) implementations have a high degree of variability in their details. Parameters, encoding methods, and dynamics formulas all differ in the literature, and some GRN implementations have a high degree of model complexity. In this paper, we present a comparative study of different implementations of a GRN and introduce new variants for comparison. We use a modified Genetic Algorithm (GA) to evaluate GRN performance on a number of common benchmark tasks, with a focus on real-time control problems. We propose an encoding scheme and set of dynamics equations that simplifies implementation and evaluate the evolutionary fitness of this proposed method. Lastly, we use the comparative modifications study to demonstrate overall enhancements for GRN models.

引用

页码：91 / 98

页数：8

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