| 1. |
- Bagheri, Zahra, et al.
(författare)
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Robustness and real-time performance of an insect inspired target tracking algorithm under natural conditions
- 2015
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Ingår i: Proceedings - 2015 IEEE Symposium Series on Computational Intelligence, SSCI 2015. - 9781479975600 ; , s. 97-102
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Konferensbidrag (refereegranskat)abstract
- Many computer vision tasks require the implementation of robust and efficient target tracking algorithms. Furthermore, in robotic applications these algorithms must perform whilst on a moving platform (ego motion). Despite the increase in computational processing power, many engineering algorithms are still challenged by real-Time applications. In contrast, lightweight and low-power flying insects, such as dragonflies, can readily chase prey and mates within cluttered natural environments, deftly selecting their target amidst distractors (swarms). In our laboratory, we record from 'target-detecting' neurons in the dragonfly brain that underlie this pursuit behavior. We recently developed a closed-loop target detection and tracking algorithm based on key properties of these neurons. Here we test our insect-inspired tracking model in open-loop against a set of naturalistic sequences and compare its efficacy and efficiency with other state-of-The-Art engineering models. In terms of tracking robustness, our model performs similarly to many of these trackers, yet is at least 3 times more efficient in terms of processing speed.
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| 2. |
- Gabrielsson, Patrick, et al.
(författare)
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High-frequency equity index futures trading using recurrent reinforcement learning with candlesticks
- 2015
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Ingår i: Proceedings - 2015 IEEE Symposium Series on Computational Intelligence, SSCI 2015. - : IEEE. - 9781479975600 ; , s. 734-741
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Konferensbidrag (refereegranskat)abstract
- In 1997, Moody and Wu presented recurrent reinforcement learning (RRL) as a viable machine learning method within algorithmic trading. Subsequent research has shown a degree of controversy with regards to the benefits of incorporating technical indicators in the recurrent reinforcement learning framework. In 1991, Nison introduced Japanese candlesticks to the global research community as an alternative to employing traditional indicators within the technical analysis of financial time series. The literature accumulated over the past two and a half decades of research contains conflicting results with regards to the utility of using Japanese candlestick patterns to exploit inefficiencies in financial time series. In this paper, we combine features based on Japanese candlesticks with recurrent reinforcement learning to produce a high-frequency algorithmic trading system for the E-mini S&P 500 index futures market. Our empirical study shows a statistically significant increase in both return and Sharpe ratio compared to relevant benchmarks, suggesting the existence of exploitable spatio-Temporal structure in Japanese candlestick patterns and the ability of recurrent reinforcement learning to detect and take advantage of this structure in a high-frequency equity index futures trading environment.
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| 3. |
- Glorieux, Emile, et al.
(författare)
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Improved Constructive Cooperative Coevolutionary Differential Evolution for Large-Scale Optimisation
- 2016
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Ingår i: Computational Intelligence, 2015 IEEE Symposium Series on. - : IEEE. - 9781479975600 ; , s. 1703-1710
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Konferensbidrag (refereegranskat)abstract
- The Differential Evolution (DE) algorithm is widely used for real-world global optimisation problems in many different domains. To improve DE's performance on large-scale optimisation problems, it has been combined with the Cooperative Coevolution (CCDE) algorithm. CCDE adopts a divide-and-conquer strategy to optimise smaller subcomponents separately instead of tackling the large-scale problem at once. DE then evolves a separate subpopulation for each subcomponent but there is cooperation between the subpopulations to co-adapt the individuals of the subpopulations with each other. The Constructive Cooperative Coevolution (C3DE) algorithm, previously proposed by the authors, is an extended version of CCDE that has a better performance on large-scale problems, interestingly also on non-separable problems. This paper proposes a new version, called the Improved Constructive Cooperative Coevolutionary Differential Evolution (C3iDE), which removes several limitations with the previous version. A novel element of C3iDE is the advanced initialisation of the subpopulations. C3iDE initially optimises the subpopulations in a partially co-adaptive fashion. During the initial optimisation of a subpopulation, only a subset of the other subcomponents is considered for the co-adaptation. This subset increases stepwise until all subcomponents are considered. The experimental evaluation of C3iDE on 36 high-dimensional benchmark functions (up to 1000 dimensions) shows an improved solution quality on large-scale global optimisation problems compared to CCDE and DE. The greediness of the co-adaptation with C3iDE is also investigated in this paper.
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| 4. |
- Glorieux, E., et al.
(författare)
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Improved Constructive Cooperative Coevolutionary Differential Evolution for Large-Scale Optimisation
- 2015
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Ingår i: Proceedings - 2015 IEEE Symposium Series on Computational Intelligence, SSCI 2015. - : IEEE. - 9781479975600 ; , s. 1703-1710
-
Konferensbidrag (refereegranskat)abstract
- The Differential Evolution (DE) algorithm is widely used for real-world global optimisation problems in many different domains. To improve DE's performance on large-scale optimisation problems, it has been combined with the Cooperative Coevolution (CCDE) algorithm. CCDE adopts a divide-and-conquer strategy to optimise smaller subcomponents separately instead of tackling the large-scale problem at once. DE then evolves a separate subpopulation for each subcomponent but there is cooperation between the subpopulations to co-adapt the individuals of the subpopulations with each other. The Constructive Cooperative Coevolution ((CDE)-D-3) algorithm, previously proposed by the authors, is an extended version of CCDE that has a better performance on large-scale problems, interestingly also on non-separable problems. This paper proposes a new version, called the Improved Constructive Cooperative Coevolutionary Differential Evolution ((CDE)-D-3i), which removes several limitations with the previous version. A novel element of (CDE)-D-3i is the advanced initialisation of the subpopulations. (CDE)-D-3i initially optimises the subpopulations in a partially co-adaptive fashion. During the initial optimisation of a subpopulation, only a subset of the other subcomponents is considered for the co-adaptation. This subset increases stepwise until all subcomponents are considered. The experimental evaluation of (CDE)-D-3i on 36 high-dimensional benchmark functions (up to 1000 dimensions) shows an improved solution quality on large-scale global optimisation problems compared to CCDE and DE. The greediness of the co-adaptation with (CDE)-D-3i is also investigated in this paper.
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