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Publications (10 of 12) Show all publications
Suero, M., Gassen, C. P., Mitic, D., Xiong, N. & Leon, M. (2020). A Deep Neural Network Model for Music Genre Recognition. In: Advances in Intelligent Systems and Computing, vol. 1074: . Paper presented at 15th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2019, co-located with the 5th International Conference on Harmony Search, Soft Computing and Applications, ICHSA 2019; Kunming; China; 20 July 2019 through 22 July 2019 (pp. 377-384). Springer
Open this publication in new window or tab >>A Deep Neural Network Model for Music Genre Recognition
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2020 (English)In: Advances in Intelligent Systems and Computing, vol. 1074, Springer , 2020, p. 377-384Conference paper, Published paper (Refereed)
Abstract [en]

Convolutional neural networks (CNNs) have become increasingly important to deal with many image processing and pattern recognition problems. In order to use CNNs in music genre recognition, spectrograms (visual representation of the spectrum of frequencies of a signal as it varies with time) are usually employed as inputs of the network. Yet some other approaches used music features for genre classification as well. In this paper we propose a new deep network model combining CNN with a simple multi-layer neural network for music genre classification. Since other features are taken into account in the multi-layer network, the combined deep neural network has shown better accuracy than each of the single models in the experiments (Code available at: https://github.com/risengnom/Music-Genre-Recognition.).

Place, publisher, year, edition, pages
Springer, 2020
National Category
Computer Systems
Identifiers
urn:nbn:se:mdh:diva-46637 (URN)10.1007/978-3-030-32456-8_41 (DOI)2-s2.0-85077008252 (Scopus ID)9783030324551 (ISBN)
Conference
15th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2019, co-located with the 5th International Conference on Harmony Search, Soft Computing and Applications, ICHSA 2019; Kunming; China; 20 July 2019 through 22 July 2019
Available from: 2020-01-02 Created: 2020-01-02 Last updated: 2020-01-02Bibliographically approved
Leon, M., Xiong, N., Molina, D. & Herrera, F. (2019). A Novel Memetic Framework for Enhancing Differential Evolution Algorithms via Combination With Alopex Local Search. International Journal of Computational Intelligence Systems, 12(2), 795-808
Open this publication in new window or tab >>A Novel Memetic Framework for Enhancing Differential Evolution Algorithms via Combination With Alopex Local Search
2019 (English)In: International Journal of Computational Intelligence Systems, ISSN 1875-6891, E-ISSN 1875-6883, Vol. 12, no 2, p. 795-808Article in journal (Refereed) Published
Abstract [en]

Differential evolution (DE) represents a class of population-based optimization techniques that uses differences of vectors to search for optimal solutions in the search space. However, promising solutions/ regions are not adequately exploited by a traditional DE algorithm. Memetic computing has been popular in recent years to enhance the exploitation of global algorithms via incorporation of local search. This paper proposes a new memetic framework to enhance DE algorithms using Alopex Local Search (MFDEALS). The novelty of the proposed MFDEALS framework lies in that the behavior of exploitation (by Alopex local search) can be controlled based on the DE global exploration status (population diversity and search stage). Additionally, an adaptive parameter inside the Alopex local search enables smooth transition of its behavior from exploratory to exploitative during the search process. A study of the important components of MFDEALS shows that there is a synergy between them. MFDEALS has been integrated with both the canonical DE method and the adaptive DE algorithm L-SHADE, leading to the MDEALS and ML-SHADEALS algorithms, respectively. Both algorithms were tested on the benchmark functions from the IEEE CEC'2014 Conference. The experiment results show that Memetic Differential Evolution with Alopex Local Search (MDEALS) not only improves the original DE algorithm but also outperforms other memetic DE algorithms by obtaining better quality solutions. Further, the comparison between ML-SHADEALS and L-SHADE demonstrates that applying the MFDEALS framework with Alopex local search can significantly enhance the performance of L-SHADE. 

Place, publisher, year, edition, pages
ATLANTIS PRESS, 2019
Keywords
Differential evolution, L-SHADE, Memetic algorithm, Alopex, Local search, Optimization
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:mdh:diva-45267 (URN)10.2991/ijcis.d.190711.001 (DOI)000483992100029 ()2-s2.0-85073320189 (Scopus ID)
Available from: 2019-09-19 Created: 2019-09-19 Last updated: 2019-10-31Bibliographically approved
Tidare, J., Leon, M., Xiong, N. & Åstrand, E. (2019). Discriminating EEG spectral power related to mental imagery of closing and opening of hand. In: 2019 9TH INTERNATIONAL IEEE/EMBS CONFERENCE ON NEURAL ENGINEERING (NER): . Paper presented at 9th IEEE/EMBS International Conference on Neural Engineering (NER), MAR 20-23, 2019, San Francisco, CA (pp. 307-310). IEEE
Open this publication in new window or tab >>Discriminating EEG spectral power related to mental imagery of closing and opening of hand
2019 (English)In: 2019 9TH INTERNATIONAL IEEE/EMBS CONFERENCE ON NEURAL ENGINEERING (NER), IEEE , 2019, p. 307-310Conference paper, Published paper (Refereed)
Abstract [en]

ElectroEncephaloGram (EEG) spectral power has been extensively used to classify Mental Imagery (MI) of movements involving different body parts. However, there is an increasing need to enable classification of MI of movements within the same limb. In this work, EEG spectral power was recorded in seven subjects while they performed MI of closing (grip) and opening (extension of fingers) the hand. The EEG data was analyzed and the feasibility of classifying MI of the two movements were investigated using two different classification algorithms, a linear regression and a Convolutional Neural Network (CNN). Results show that only the CNN is able to significantly classify MI of opening and closing of the hand with an average classification accuracy of 60.4%. This indicates the presence of higher-order non-linear discriminatory information and demonstrates the potential of using CNN in classifying MI of same-limb movements.

Place, publisher, year, edition, pages
IEEE, 2019
Series
International IEEE EMBS Conference on Neural Engineering, ISSN 1948-3546
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:mdh:diva-44330 (URN)10.1109/NER.2019.8717059 (DOI)000469933200077 ()2-s2.0-85066765799 (Scopus ID)978-1-5386-7921-0 (ISBN)
Conference
9th IEEE/EMBS International Conference on Neural Engineering (NER), MAR 20-23, 2019, San Francisco, CA
Available from: 2019-06-20 Created: 2019-06-20 Last updated: 2020-02-04Bibliographically approved
Leon, M., Ballesteros, J., Tidare, J., Xiong, N. & Åstrand, E. (2019). Feature Selection of EEG Oscillatory Activity Related to Motor Imagery Using a Hierarchical Genetic Algorithm. In: 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings: . Paper presented at 2019 IEEE Congress on Evolutionary Computation, CEC 2019, 10 June 2019 through 13 June 2019 (pp. 87-94). Institute of Electrical and Electronics Engineers Inc.
Open this publication in new window or tab >>Feature Selection of EEG Oscillatory Activity Related to Motor Imagery Using a Hierarchical Genetic Algorithm
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2019 (English)In: 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings, Institute of Electrical and Electronics Engineers Inc. , 2019, p. 87-94Conference paper, Published paper (Refereed)
Abstract [en]

Motor Imagery (MI) classification from neural activity is thought to represent valuable information that can be provided as real-time feedback during rehabilitation after for example a stroke. Previous studies have suggested that MI induces partly subject-specific EEG activation patterns, suggesting that individualized classification models should be created. However, due to fatigue of the user, only a limited number of samples can be recorded and, for EEG recordings, each sample is often composed of a large number of features. This combination leads to an undesirable input data set for classification. In order to overcome this constraint, we propose a new methodology to create and select features from the EEG signal in two steps. First, the input data is divided into different windows to reduce the cardinality of the input. Secondly, a Hierarchical Genetic Algorithm is used to select relevant features using a novel fitness function which combines the data reduction with a correlation feature selection measure. The methodology has been tested on EEG oscillatory activity recorded from 6 healthy volunteers while they performed an MI task. Results have successfully proven that a classification above 75% can be obtained in a restrictive amount of time (0.02 s), reducing the number of features by almost 90%.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc., 2019
Keywords
EEG Signal, Hierarchical Genetic Algorithm, Motor Imagery, Classification (of information), Data reduction, Genetic algorithms, Input output programs, Neurons, Activation patterns, Classification models, Correlation features, EEG signals, Healthy volunteers, Real-time feedback, Feature extraction
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:mdh:diva-46543 (URN)10.1109/CEC.2019.8789948 (DOI)000502087100013 ()2-s2.0-85071317681 (Scopus ID)9781728121536 (ISBN)
Conference
2019 IEEE Congress on Evolutionary Computation, CEC 2019, 10 June 2019 through 13 June 2019
Available from: 2019-12-17 Created: 2019-12-17 Last updated: 2020-01-02Bibliographically approved
Leon, M. (2019). IMPROVING DIFFERENTIAL EVOLUTION WITH ADAPTIVE AND LOCAL SEARCH METHODS. (Doctoral dissertation). Västerås: Mälardalen University
Open this publication in new window or tab >>IMPROVING DIFFERENTIAL EVOLUTION WITH ADAPTIVE AND LOCAL SEARCH METHODS
2019 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Differential Evolution (DE) is a population-based algorithm that belongs to the Evolutionary algorithm family. During recent years, DE has become a popular algorithm in optimization due to its strength solving different types of optimization problems and due to its easy usage and implementation.

However, how to choose proper mutation strategy and control parameters for DE presents a major difficulty in many real applications. Since both mutation strategy and DE control parameters are highly problem dependent, they have to be adapted to suite different search spaces and different problems. Failure in proper assignment for them will cause slow convergence in search or stagnation with a local optimum.

Many researches have been conducted to tackle the above issues. The major efforts have been made in the following three directions. First, some works have been proposed that adapt the selection between various mutation strategies. But the choice of strategies in these methods has not considered the difference of quality of individuals in the population, which means that all individuals will acquire the same probability to select a mutation strategy from the candidates. This does not seem a very desired practice since solutions of large difference would require different mutation operators to reach improvement. Second, many works have been focusing on the adaptation of the control parameters of DE (mutation factor (F) and crossover rate (CR)). They mainly rely on previous successful F and CR values to update the probability functions that are used to generate new F and CR values. By doing this, they ignore the stochastic nature of the operators in DE such that weak F and CR values can also get success in producing better trial solutions. The use of such imprecise experiences of success would prevent the DE parameters from being adapted towards the most effective values in coming generations. Third, various local search methods have been incorporated into DE to enhance exploitation in promising regions so as to speed up the convergence to optima. It is important to properly adjust the characteristics of the local search in DE to achieve well balanced exploratory/exploitative behavior to solve complex optimization problems.

This thesis aims to further improve the performance of DE by new adaptation and local search methods. The main results can be summarized in the following three aspects:

1) Proposal of a new rank-based mutation adaptation method, which takes into account the quality of solutions in the population when adapting the selection probabilities of mutation strategies. This makes possible to treat solutions with distinct ranks (in quality) differently by using different selection probabilities for mutation operators.

2) Development of improved parameter adaptation methods for DE, which emphasizes more reliable and fair evaluation of candidates (F and CR assignments) during the search process. It is suggested that greedy search being used as a fast and cheap technique to look for better parameter assignment for F and CR respectively in the neighborhood of a current candidate. Further, a joint parameter adaptation method is proposed that enables continuous update of the selection probabilities for F and CR pairs based on feedback acquired during the search.

3) Proposal of new methods for better incorporation of local search into a DE algorithm. The Eager Random Search method is investigated as local search inside DE, which exhibits different exploratory-exploitative characteristics by using different probability density functions. More importantly, we propose a novel memetic framework in which Alopex local search (ALS) is performed in collaboration with a DE algorithm. The framework favors seamless connection between exploration and exploitation in the sense that the behavior of exploitation by ALS can be controlled by the status of global exploration by DE.

The proposed methods and algorithms have been tested in a number of benchmark problems, obtaining competitive results compared with the state-of-the-art algorithms. Additionally, the Greedy Adaptive DE (GADE) algorithm (developed based on greedy search for DE parameters) has been tested in a real industrial problem, i.e., finding best component parameters to optimize the performance of harmonic filters for power transmission. GADE is shown to produce better harmonic filter systems with lower harmonic distortion than the standard DE.

Place, publisher, year, edition, pages
Västerås: Mälardalen University, 2019. p. 130
Series
Mälardalen University Press Dissertations, ISSN 1651-4238 ; 302
Keywords
Differential Evolution; Adaptation; Memetic algorithm; Evolutionary Algorithm
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:mdh:diva-45868 (URN)978-91-7485-447-3 (ISBN)
Public defence
2019-12-18, Delta, Mälardalens högskola, Västerås, 13:15 (English)
Opponent
Supervisors
Funder
Knowledge Foundation, 16317
Available from: 2019-10-29 Created: 2019-10-29 Last updated: 2019-11-15Bibliographically approved
Ahlberg, C., Leon, M., Ekstrand, F. & Ekström, M. (2019). Unbounded Sparse Census Transform using Genetic Algorithm. In: 2019 IEEE WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION (WACV): . Paper presented at 19th IEEE Winter Conference on Applications of Computer Vision (WACV), JAN 07-11, 2019, Waikoloa Village, HI (pp. 1616-1625). IEEE
Open this publication in new window or tab >>Unbounded Sparse Census Transform using Genetic Algorithm
2019 (English)In: 2019 IEEE WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION (WACV), IEEE , 2019, p. 1616-1625Conference paper, Published paper (Refereed)
Abstract [en]

The Census Transform (CT) is a well proven method for stereo vision that provides robust matching, with respect to object boundaries, outliers and radiometric distortion, at a low computational cost. Recent CT methods propose patterns for pixel comparison and sparsity, to increase matching accuracy and reduce resource requirements. However, these methods are bounded with respect to symmetry and/or edge length. In this paper, a Genetic algorithm (GA) is applied to find a new and powerful CT method. The proposed method, Genetic Algorithm Census Transform (GACT), is compared with the established CT methods, showing better results for benchmarking datasets. Additional experiments have been performed to study the search space and the correlation between training and evaluation data.

Place, publisher, year, edition, pages
IEEE, 2019
Series
IEEE Winter Conference on Applications of Computer Vision, ISSN 2472-6737
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:mdh:diva-44332 (URN)10.1109/WACV.2019.00177 (DOI)000469423400170 ()2-s2.0-85063571752 (Scopus ID)978-1-7281-1975-5 (ISBN)
Conference
19th IEEE Winter Conference on Applications of Computer Vision (WACV), JAN 07-11, 2019, Waikoloa Village, HI
Available from: 2019-06-20 Created: 2019-06-20 Last updated: 2019-12-18Bibliographically approved
Leon, M. & Xiong, N. (2018). Enhancing Adaptive Differential Evolution Algorithms with Rank-Based Mutation Adaptation. In: 2018 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC): . Paper presented at IEEE Congress on Evolutionary Computation (IEEE CEC) as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI) (pp. 103-109). IEEE
Open this publication in new window or tab >>Enhancing Adaptive Differential Evolution Algorithms with Rank-Based Mutation Adaptation
2018 (English)In: 2018 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), IEEE , 2018, p. 103-109Conference paper, Published paper (Refereed)
Abstract [en]

Differential evolution has many mutation strategies which are problem dependent. Some Adaptive Differential Evolution techniques have been proposed tackling this problem. But therein all individuals are treated equally without taking into account how good these solutions are. In this paper, a new method called Ranked-based Mutation Adaptation (RAM) is proposed, which takes into consideration the ranking of an individual in the whole population. This method will assign different probabilities of choosing different mutation strategies to different groups in which the population is divided. RAM has been integrated into several well-known adaptive differential evolution algorithms and its performance has been tested on the benchmark suit proposed in CEC2014. The experimental results shows the use of RAM can produce generally better quality solutions than the original adaptive algorithms.

Place, publisher, year, edition, pages
IEEE, 2018
Keywords
Evolutionary Algorithm, Differential Evolution, Mutation strategy, Adaptation
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:mdh:diva-41825 (URN)10.1109/CEC.2018.8477879 (DOI)000451175500015 ()2-s2.0-85056286107 (Scopus ID)
Conference
IEEE Congress on Evolutionary Computation (IEEE CEC) as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)
Available from: 2018-12-27 Created: 2018-12-27 Last updated: 2020-01-10Bibliographically approved
Ramos, J., Leon, M. & Xiong, N. (2018). MPADE: An Improved Adaptive Multi-Population Differential Evolution Algorithm Based on JADE. In: 2018 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC): . Paper presented at IEEE Congress on Evolutionary Computation (IEEE CEC) as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI) (pp. 1139-1146). IEEE
Open this publication in new window or tab >>MPADE: An Improved Adaptive Multi-Population Differential Evolution Algorithm Based on JADE
2018 (English)In: 2018 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), IEEE , 2018, p. 1139-1146Conference paper, Published paper (Refereed)
Abstract [en]

JADE is an state-of-the-art adaptive differential evolution algorithm which implements "DE/current-to-pbest" as its mutation strategy, adapts its mutation factor and crossover rate, and uses an optional external archive to keep track of potential removed individuals in previous generations. This paper proposes MPADE, which extends JADE by using a multi-populated approach to solve high dimensional real-parameter optimization problems. This mechanism helps preventing the two well-known problems affecting the differential evolution algorithm performance, which are premature convergence and stagnation. The algorithm was tested using the benchmark functions in IEEE Congress on Evolutionary Computation 2014 test suite. MPADE was compared using Wilcoxon test to JADE algorithm and with other state-of-the-art algorithms that either use a multi-population approach or adapt their parameters. The experimental results show that the proposed new algorithm improves significantly its precursor and it is also suggested that other state-of-the-art algorithms could benefit from the multi-populated based approach.

Place, publisher, year, edition, pages
IEEE, 2018
Keywords
Differential evolution, multi-population, coarse-grained parallel, population topology, adaptive parameter, global optimization, evolutionary optimization
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:mdh:diva-41826 (URN)10.1109/CEC.2018.8477764 (DOI)000451175500146 ()2-s2.0-85056280928 (Scopus ID)
Conference
IEEE Congress on Evolutionary Computation (IEEE CEC) as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)
Available from: 2018-12-27 Created: 2018-12-27 Last updated: 2020-01-10Bibliographically approved
Leon, M. & Xiong, N. (2017). Alopex-based mutation strategy in Differential Evolution. In: 2017 IEEE Congress on Evolutionary Computation, CEC 2017 - Proceedings: . Paper presented at 2017 IEEE Congress on Evolutionary Computation, CEC 2017; Donostia-San Sebastian; Spain; 5 June 2017 through 8 June 2017; Category numberCFP17ICE-ART; Code 129053 (pp. 1978-1984). Institute of Electrical and Electronics Engineers Inc.
Open this publication in new window or tab >>Alopex-based mutation strategy in Differential Evolution
2017 (English)In: 2017 IEEE Congress on Evolutionary Computation, CEC 2017 - Proceedings, Institute of Electrical and Electronics Engineers Inc. , 2017, p. 1978-1984Conference paper, Published paper (Refereed)
Abstract [en]

Differential Evolution represents a class of evolutionary algorithms that are highly competitive for solving numerical optimization problems. In a Differential Evolution algorithm, there are a few alternative mutation strategies, which may lead to good or a bad performance depending on the property of the problem. A new mutation strategy, called DE/Alopex/1, is proposed in this paper. This mutation strategy distinguishes itself from other mutation strategies in that it uses the fitness values of the individuals in the population in order to calculate the probabilities of move directions. The performance of DE/Alopex/1 has been evaluated on the benchmark suite from CEC2013. The results of the experiments show that DE/Alopex/1 outperforms some state-of-the-art mutation strategies. © 2017 IEEE.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc., 2017
Keywords
Alopex, Differential Evolution, Evolutionary Algorithm, Mutation strategy
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:mdh:diva-36297 (URN)10.1109/CEC.2017.7969543 (DOI)000426929700256 ()2-s2.0-85027852098 (Scopus ID)9781509046010 (ISBN)
Conference
2017 IEEE Congress on Evolutionary Computation, CEC 2017; Donostia-San Sebastian; Spain; 5 June 2017 through 8 June 2017; Category numberCFP17ICE-ART; Code 129053
Available from: 2017-08-31 Created: 2017-08-31 Last updated: 2018-03-29Bibliographically approved
Leon, M. & Xiong, N. (2016). Adapting differential evolution algorithms for continuous optimization via greedy adjustment of control parameters. Journal of Artificial Intelligence and Soft Computing Research, 6(2), 103-118
Open this publication in new window or tab >>Adapting differential evolution algorithms for continuous optimization via greedy adjustment of control parameters
2016 (English)In: Journal of Artificial Intelligence and Soft Computing Research, ISSN 2449-6499, Vol. 6, no 2, p. 103-118Article in journal (Refereed) Published
Abstract [en]

Differential evolution (DE) presents a class of evolutionary and meta-heuristic techniques that have been applied successfully to solve many real-world problems. However, the performance of DE is significantly influenced by its control parameters such as scaling factor and crossover probability. This paper proposes a new adaptive DE algorithm by greedy adjustment of the control parameters during the running of DE. The basic idea is to perform greedy search for better parameter assignments in successive learning periods in the whole evolutionary process. Within each learning period, the current parameter assignment and its neighboring assignments are tested (used) in a number of times to acquire a reliable assessment of their suitability in the stochastic environment with DE operations. Subsequently the current assignment is updated with the best candidate identified from the neighborhood and the search then moves on to the next learning period. This greedy parameter adjustment method has been incorporated into basic DE, leading to a new DE algorithm termed as Greedy Adaptive Differential Evolution (GADE). GADE has been tested on 25 benchmark functions in comparison with five other DE variants. The results of evaluation demonstrate that GADE is strongly competitive: it obtained the best rank among the counterparts in terms of the summation of relative errors across the benchmark functions with a high dimensionality.

Keywords
Differential evolution, Optimization, Parameter adaptation
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:mdh:diva-34771 (URN)10.1515/jaiscr-2016-0009 (DOI)000408865800004 ()2-s2.0-85009725987 (Scopus ID)
Available from: 2017-02-08 Created: 2017-02-02 Last updated: 2019-10-29Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-3425-3837

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