Evolutionary reinforcement learning: A survey
Reinforcement learning (RL) is a machine learning approach that trains agents to maximize
cumulative rewards through interactions with environments. The integration of RL with deep …
cumulative rewards through interactions with environments. The integration of RL with deep …
Cooperative coevolution of Elman recurrent neural networks for chaotic time series prediction
Cooperative coevolution decomposes a problem into subcomponents and employs
evolutionary algorithms for solving them. Cooperative coevolution has been effective for …
evolutionary algorithms for solving them. Cooperative coevolution has been effective for …
Competition and collaboration in cooperative coevolution of Elman recurrent neural networks for time-series prediction
R Chandra - IEEE transactions on neural networks and learning …, 2015 - ieeexplore.ieee.org
Collaboration enables weak species to survive in an environment where different species
compete for limited resources. Cooperative coevolution (CC) is a nature-inspired …
compete for limited resources. Cooperative coevolution (CC) is a nature-inspired …
Co-evolutionary multi-task learning with predictive recurrence for multi-step chaotic time series prediction
Multi-task learning employs a shared representation of knowledge for learning several
instances of the same problem. Multi-step time series problem is one of the most challenging …
instances of the same problem. Multi-step time series problem is one of the most challenging …
Scale insensitive and focus driven mobile screen defect detection in industry
With the wide-spread of smartphones, mobile phone screen has become an important IO
device in HCI and its quality is of great matter in interaction. Traditional defect detection …
device in HCI and its quality is of great matter in interaction. Traditional defect detection …
Evaluation of co-evolutionary neural network architectures for time series prediction with mobile application in finance
R Chandra, S Chand - Applied Soft Computing, 2016 - Elsevier
The fusion of soft computing methods such as neural networks and evolutionary algorithms
have given a very promising performance for time series prediction problems. In order to …
have given a very promising performance for time series prediction problems. In order to …
Co-evolutionary multi-task learning for dynamic time series prediction
Time series prediction typically consists of a data reconstruction phase where the time series
is broken into overlapping windows known as the timespan. The size of the timespan can be …
is broken into overlapping windows known as the timespan. The size of the timespan can be …
Classification of tea specimens using novel hybrid artificial intelligence methods
Two innovative systems based on feed-forward and recurrent neural network used for
qualitative analysis has been applied to specimens of different fruit tea. Their performance …
qualitative analysis has been applied to specimens of different fruit tea. Their performance …
[PDF][PDF] Approximation of phenol concentration using novel hybrid computational intelligence methods
P Plawiak, R Tadeusiewicz - International Journal of Applied …, 2014 - sciendo.com
This paper presents two innovative evolutionary-neural systems based on feed-forward and
recurrent neural networks used for quantitative analysis. These systems have been applied …
recurrent neural networks used for quantitative analysis. These systems have been applied …
On the issue of separability for problem decomposition in cooperative neuro-evolution
Cooperative coevolution divides an optimisation problem into subcomponents and employs
evolutionary algorithms for evolving them. Problem decomposition has been a major issue …
evolutionary algorithms for evolving them. Problem decomposition has been a major issue …