Evolutionary multi-objective generation of recurrent neural network ensembles for time series prediction

Smith, Christopher P.; Jin, Yaochu

doi:10.1016/j.neucom.2014.05.062

Cited by 104 publications

(33 citation statements)

References 48 publications

(25 reference statements)

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“…For example, a knee point, around which a small improvement of any objective causes a large degeneration of others, is always of interest to DMs as an implicit preferred solution [67][68][69]. Examples include model selection in machine learning [70,71] and sparse reconstruction [72].…”

Section: Implicit Preferencesmentioning

confidence: 99%

A mini-review on preference modeling and articulation in multi-objective optimization: current status and challenges

Wang

Olhofer

Jin

2017

Complex Intell. Syst.

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122

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Evolutionary multi-objective optimization aims to provide a representative subset of the Pareto front to decision makers. In practice, however, decision makers are usually interested in only a particular part of the Pareto front of the multi-objective optimization problem. This is particularly true when the number of objectives becomes large. Over the past decade, preference-based multi-objective optimization has attracted increasing attention from both academia and industry due to its significance in both theory and practice. Significant progress has been made in evolutionary multiobjective optimization and multi-criteria decision communities, although many open issues still remain to be addressed. This paper provides a concise review on preference-based multi-objective optimization, including various preference modeling methods and existing preference-based optimization methods, as well as a brief discussion of the main future challenges.

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Section: Implicit Preferencesmentioning

confidence: 99%

A mini-review on preference modeling and articulation in multi-objective optimization: current status and challenges

Wang

Olhofer

Jin

2017

Complex Intell. Syst.

Self Cite

122

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“…A multi-objective stock index tracking methodology was proposed by Ni and Wang (2013) based on profitability, stability and volatility of stocks. The domain has recently been extended via the generation of an ensemble of recurrent neural networks via evolutionary multi-objective GA that tune the recurrent neural architecture with the best set of models selected on the basis of a Pareto front (Smith & Jin, 2014). In a similar cash flow prediction domain, Weighted SVM and fuzzy logic were combined-mapped to "fastmessy" GA chromosomes in a bid to address vagueness via fuzzy logic and improve temporal prediction via improved input/output mapping (Cheng & Roy, 2011).…”

Section: Literature Reviewmentioning

confidence: 99%

Development of a genetic programming-based GA methodology for the prediction of short-to-medium-term stock markets

Alghieth

Yang

Chiclana

2016

2016 IEEE Congress on Evolutionary Computation (CEC)

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Abstract-This research presents a specialised extension to the genetic algorithms (GA) known as the genetic programming (GP) and gene expression programming (GEP) to explore and investigate the outcome of the GEP criteria on the stock market price prediction. The aim of this research is to model and predict short-to-medium term stock value fluctuations in the market via genetically tuned stock market parameters. The technology proposes a fractional adaptive mutation rate Elitism (GEP-FAMR) technique to initiate a balance between varied mutation rates and between varied-fitness chromosomes, thereby improving prediction accuracy and fitness improvement rate. The methodology is evaluated against different dataset and selection methods and showed promising results with a low error-rate in the resultant pattern matching with an overall accuracy of 95.96% for short-term 5-day and 95.35% for medium-term 56-day trading periods.

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“…The Pareto-based approach has witnessed great success in evolutionary multi-objective optimization [42], [43] as well as in evolutionary multi-objective learning [44]. Thus, it is natural to adopt the Pareto-based evolutionary approach to optimize the combination of functionals in the Trace transform for extracting robust image features.…”

Section: Evolutionary Multi-objective Optimization Of Trace Tranmentioning

confidence: 99%

Evolutionary Multiobjective Image Feature Extraction in the Presence of Noise

Albukhanajer

Briffa

Jin

2015

IEEE Trans. Cybern.

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Abstract-A Pareto-based evolutionary multi-objective approach is adopted to optimize the functionals in the Trace Transform for extracting image features that are robust to noise and invariant to geometric deformations such as rotation, scale and translation (RST). To this end, sample images with noise and with RST distortion are employed in the evolutionary optimization of the Trace Transform, which is termed evolutionary Trace Transform with noise (ETTN). Experimental studies on a fish image database and the Columbia COIL-20 image database show that the ETTN optimized on a few low-resolution images from the fish database can extract robust and RST invariant features from the standard images in the fish database as well as in the COIL-20 database. These results demonstrate that the proposed ETTN is very promising in that it is computationally efficient, invariant to RST deformation, robust to noise and generalizable.

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Evolutionary multi-objective generation of recurrent neural network ensembles for time series prediction

Cited by 104 publications

References 48 publications

A mini-review on preference modeling and articulation in multi-objective optimization: current status and challenges

A mini-review on preference modeling and articulation in multi-objective optimization: current status and challenges

Development of a genetic programming-based GA methodology for the prediction of short-to-medium-term stock markets

Evolutionary Multiobjective Image Feature Extraction in the Presence of Noise

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