Affiliation:
1. 1 Computer Department , Polish Naval Academy , ul. Smidowicza 69, 81-127 Gdynia , Poland
Abstract
Abstract
Modularity is a feature of most small, medium and large–scale living organisms that has evolved over many years of evolution. A lot of artificial systems are also modular, however, in this case, the modularity is the most frequently a consequence of a handmade design process. Modular systems that emerge automatically, as a result of a learning process, are very rare. What is more, we do not know mechanisms which result in modularity. The main goal of the paper is to continue the work of other researchers on the origins of modularity, which is a form of optimal organization of matter, and the mechanisms that led to the spontaneous formation of modular living forms in the process of evolution in response to limited resources and environmental variability. The paper focuses on artificial neural networks and proposes a number of mechanisms operating at the genetic level, both those borrowed from the natural world and those designed by hand, the use of which may lead to network modularity and hopefully to an increase in their effectiveness. In addition, the influence of external factors on the shape of the networks, such as the variability of tasks and the conditions in which these tasks are performed, is also analyzed. The analysis is performed using the Hill Climb Assembler Encoding constructive neuro-evolutionary algorithm. The algorithm was extended with various module-oriented mechanisms and tested under various conditions. The aim of the tests was to investigate how individual mechanisms involved in the evolutionary process and factors external to this process affect modularity and efficiency of neural networks.
Subject
Artificial Intelligence,Computer Vision and Pattern Recognition,Hardware and Architecture,Modeling and Simulation,Information Systems
Reference50 articles.
1. S. Ahmadian, S. Jalali, S. Islam, A. Khosravi, E. Fazli, and S. Nahavandi. A novel deep neuroevolution-based image classification method to diagnose coronavirus disease (covid-19). Comput Biol Med., (139:104994), 2021.10.1016/j.compbiomed.2021.104994855814934749098
2. A. Baldominos, Y. Saez, and P. Isasi. Evolutionary convolutional neural networks: an application to handwriting recognition. Neurocomputing, 283:38–52, 2018.
3. C.Y. Baldwin and K.B. Clark. Design Rules: The power of modularity. Chapter 3: What Is Modularity? MIT Press, 2018.
4. A. Billard and M. J. Mataric. Learning human movements by imitation: evaluation of a biologically inspired connectionist architecture. Robotics and Autonomous Systems, 941:1–16, 2001.
5. V. D. Blondel, J.-L. Guillaume, R. Lambiotte, and E. Lefebvre. Fast unfolding of communities in large networks. Journal of Statistical Mechanics Theory and Experiment, 10:10008, 2008.10.1088/1742-5468/2008/10/P10008