An Implementation and Experimental Evaluation of a Modularity Explicit Encoding Method for Neuroevolution on Complex Learning Tasks

Author(s):  
Yukai Qiao ◽  
Marcus Gallagher
2018 ◽  
Vol 53 (4) ◽  
pp. 380-389 ◽  
Author(s):  
Marie‐Laurence Tremblay ◽  
Jimmie Leppink ◽  
Gilles Leclerc ◽  
Jan‐Joost Rethans ◽  
Diana H J M Dolmans

2011 ◽  
Vol 40 (3) ◽  
pp. 623-643 ◽  
Author(s):  
B. Slof ◽  
G. Erkens ◽  
P. A. Kirschner ◽  
J. Janssen ◽  
J. G. M. Jaspers

2020 ◽  
Vol 7 (3) ◽  
pp. 118-137
Author(s):  
Sadia Nawaz ◽  
Gregor Kennedy ◽  
James Bailey ◽  
Chris Mead

Confusion is an important epistemic emotion because it can help students focus their attention and effort when solving complex learning tasks. However, unresolved confusion can be detrimental because it may result in students’ disengagement. This is especially concerning in simulation environments using discovery-based learning, which puts more of the onus for learning on the students. Thus, students with misconceptions may become confused. In this study, the possible moments of confusion in a simulation-based predict-observe-explain (POE) environment were investigated. Log-based interaction patterns of undergraduate students from a fully online course were analyzed. It was found that POE environments can offer a level of difficulty that potentially triggers some confusion, and a likely moment of students’ confusion was the observe task. It was also found that confidence in prior knowledge is an important factor that can contribute to students’ confusion. Students mostly struggled when they discovered a mismatch between the subjective and objective correctness of their responses. The effects of such a mismatch were more pronounced when confusion markers were analyzed than when students’ learning outcomes were observed. These findings may guide future works to bridge the knowledge gaps that lead to confusion in POE environments.


Genes ◽  
2018 ◽  
Vol 9 (12) ◽  
pp. 626 ◽  
Author(s):  
Xu Yang ◽  
Songgaojun Deng ◽  
Mengyao Ji ◽  
Jinfeng Zhao ◽  
Wenhao Zheng

Artificial intelligence research received more and more attention nowadays. Neural Evolution (NE) is one very important branch of AI, which waves the power of evolutionary algorithms to generate Artificial Neural Networks (ANNs). How to use the evolutionary advantages of network topology and weights to solve the application of Artificial Neural Networks is the main problem in the field of NE. In this paper, a novel DNA encoding method based on the triple codon is proposed. Additionally, a NE algorithm Triplet Codon Encoding Neural Network Evolving Algorithm (TCENNE) based on this encoding method is presented to verify the rationality and validity of the coding design. The results show that TCENNE is very effective and more robust than NE algorithms, due to the coding design. Also, it is shown that it can realize the co-evolution of network topology and weights and outperform other neural evolution systems in challenging reinforcement learning tasks.


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