ADEL: Autonomous Developmental Evolutionary Learning for Robotic Manipulation

2021 ◽  
Author(s):  
Yiming Li ◽  
Peng Wang ◽  
Xiaofei Shen ◽  
Jiayuan Liu ◽  
Haonan Duan ◽  
...  
Keyword(s):  
Robotic Manipulation ◽  
Evolutionary Learning

Robotic Manipulation of Objects

2020 ◽  
Vol 26 (2) ◽  
pp. 58-63
Author(s):  
R.R. Sosnin ◽  
Keyword(s):  
Robotic Manipulation

A Quantum-Inspired Evolutionary Learning Process to Design Dilation-Erosion Perceptrons for Financial Forecasting

2012 ◽  
Vol 10 (3) ◽  
pp. 192-201 ◽  
Author(s):  
Ricardo de A. Araújo ◽  
Adriano L. I. Oliveira ◽  
Sérgio Soares ◽  
Silvio Meira
Keyword(s):  
Learning Process ◽  
Evolutionary Learning ◽  
Financial Forecasting

scholarly journals Towards Generative Design of Computationally Efficient Mathematical Models with Evolutionary Learning

Entropy ◽  
2020 ◽  
Vol 23 (1) ◽  
pp. 28
Author(s):  
Anna V. Kalyuzhnaya ◽  
Nikolay O. Nikitin ◽  
Alexander Hvatov ◽  
Mikhail Maslyaev ◽  
Mikhail Yachmenkov ◽  
...  
Keyword(s):  
Mathematical Models ◽  
Learning Approach ◽  
Model Structure ◽  
Evolutionary Learning ◽  
Learning Models ◽  
Computationally Efficient ◽  
Performance Models ◽  
Generative Design ◽  
Computational Resources ◽  
Machine Learning Models

In this paper, we describe the concept of generative design approach applied to the automated evolutionary learning of mathematical models in a computationally efficient way. To formalize the problems of models’ design and co-design, the generalized formulation of the modeling workflow is proposed. A parallelized evolutionary learning approach for the identification of model structure is described for the equation-based model and composite machine learning models. Moreover, the involvement of the performance models in the design process is analyzed. A set of experiments with various models and computational resources is conducted to verify different aspects of the proposed approach.

scholarly journals Adverse Selection, Heterogeneous Beliefs, and Evolutionary Learning

2021 ◽  
Author(s):  
Clemens Buchen ◽  
Alberto Palermo
Keyword(s):  
Adverse Selection ◽  
Relative Size ◽  
Nonlinear Dynamic System ◽  
Heterogeneous Beliefs ◽  
Evolutionary Learning ◽  
Common Assumption ◽  
Cobweb Model ◽  
The Common ◽  
Learning Set ◽  
Set Up

AbstractWe relax the common assumption of homogeneous beliefs in principal-agent relationships with adverse selection. Principals are competitors in the product market and write contracts also on the base of an expected aggregate. The model is a version of a cobweb model. In an evolutionary learning set-up, which is imitative, principals can have different beliefs about the distribution of agents’ types in the population. The resulting nonlinear dynamic system is studied. Convergence to a uniform belief depends on the relative size of the bias in beliefs.

scholarly journals Evolutionary Learning in Signalling Games

2001 ◽  
Vol 34 (1) ◽  
pp. 34-63 ◽  
Author(s):  
Hans Jørgen Jacobsen ◽  
Mogens Jensen ◽  
Birgitte Sloth
Keyword(s):  
Evolutionary Learning ◽  
Signalling Games
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