Neuro-evolutionary system for FOREX trading

Mańdziuk, Jacek; Rajkiewicz, Piotr

doi:10.1109/cec.2016.7744384

Cited by 5 publications

(2 citation statements)

References 6 publications

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“…Semantic analysis improves the prediction rate by detecting positive and negative news headlines and predicting accordingly. Most of the other researchers have used the neural network-based models that were capable of predicting time series (e.g., Ranjit, Shruti, Sital, and Subarna [44]; Lina, Yujie, Xiao, Jinquan, Jiguo, and Chengming [45]; Jacek and Piotr [46]). The neural network was an evolutionary discovery since it predicts using hidden neurons and is also capable of adjusting the weighted values which help with providing good accuracy.…”

Section: Methodsmentioning

confidence: 99%

A Review on Recent Advancements in FOREX Currency Prediction

Islam¹,

Hossain

Rahman³

et al. 2020

Algorithms

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In recent years, the foreign exchange (FOREX) market has attracted quite a lot of scrutiny from researchers all over the world. Due to its vulnerable characteristics, different types of research have been conducted to accomplish the task of predicting future FOREX currency prices accurately. In this research, we present a comprehensive review of the recent advancements of FOREX currency prediction approaches. Besides, we provide some information about the FOREX market and cryptocurrency market. We wanted to analyze the most recent works in this field and therefore considered only those papers which were published from 2017 to 2019. We used a keyword-based searching technique to filter out popular and relevant research. Moreover, we have applied a selection algorithm to determine which papers to include in this review. Based on our selection criteria, we have reviewed 39 research articles that were published on “Elsevier”, “Springer”, and “IEEE Xplore” that predicted future FOREX prices within the stipulated time. Our research shows that in recent years, researchers have been interested mostly in neural networks models, pattern-based approaches, and optimization techniques. Our review also shows that many deep learning algorithms, such as gated recurrent unit (GRU) and long short term memory (LSTM), have been fully explored and show huge potential in time series prediction.

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Section: Methodsmentioning

confidence: 99%

A Review on Recent Advancements in FOREX Currency Prediction

Islam¹,

Hossain

Rahman³

et al. 2020

Algorithms

View full text Add to dashboard Cite

show abstract

“…to determine activation functions in [14]. Neuroevolution has been applied to many problems, including evolving platooning strategies in intelligent transportation systems [15], estimating stock closing indices using evolutionary neural networks [16], and neuro-evolutionary systems for foreign exchange trading [17].…”

Section: Neuroevolutionmentioning

confidence: 99%

Evolution of Multiobjective Neuromodulated Neurocontrollers for Multi-Robot Systems

Showalter¹

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This thesis focuses on advancing our understanding of the evolution of multiobjective neurocontrollers that have the ability to perform unsupervised learning while operating.We begin with some biologically-inspired modifications to a standard neuroevolution algorithm, that add unsupervised learning and inheritance of said learning for the benefit of offspring generations. We then explore and analyze the relationship between neurocontroller topology and function. Next, a series of modifications to improve the performance of the evolutionary algorithm, and to adapt it for coevolution are presented, along with the results of a series of experiments used to demonstrate their effectiveness.Finally, we present an experiment designed to determine the ability of the complete method to cross the boundary between simulation and reality. Synaptic plasticity has been shown to facilitate unsupervised learning by adapting neural network weights. Neuromodulation is a biologically-inspired technique that can adapt the per-connection learning rates of synaptic plasticity. Multiobjective evolution of neural network topology and weights has been used to design neurocontrollers for autonomous robots. Lamarckian inheritance has been demonstrated with neuroevolution to pass on learned behaviour from parent to offspring generations. Two previous investigations are presented here. Firstly, multiobjective evolution of network weights and topologies (NEAT-MODS) is augmented with neuromodulated iii weight connection of the pre-synaptic neuron and the post-synaptic neuron x x-axis location of robot Cartesian coordinate y y-axis location of robot Cartesian coordinate

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Detection of Important Features from Images Using Heuristic Approach

Połap

Woźniak

2017

Communications in Computer and Information Science

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Neuro-evolutionary system for FOREX trading

Cited by 5 publications

References 6 publications

A Review on Recent Advancements in FOREX Currency Prediction

A Review on Recent Advancements in FOREX Currency Prediction

Evolution of Multiobjective Neuromodulated Neurocontrollers for Multi-Robot Systems

Detection of Important Features from Images Using Heuristic Approach

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