Papers

International Journal and Conference Publications

  • [DOI] A. Nasrollahzadeh, G. Karimian, and A. Mehrafsa, “Implementation of neuro-fuzzy system with modified high performance genetic algorithm on embedded systems,” Applied soft computing, p. -, 2017.
    [Bibtex]
    @article{NKM2017,
    title = {Implementation of neuro-fuzzy system with modified high performance genetic algorithm on embedded systems},
    journal = {Applied Soft Computing},
    volume = {},
    number = {},
    pages = { - },
    year = {2017},
    note = {},
    issn = {1568-4946},
    doi = {10.1016/j.asoc.2017.07.007},
    url = {http://www.sciencedirect.com/science/article/pii/S156849461730409X},
    author = {Ali Nasrollahzadeh and Ghader Karimian and Amir Mehrafsa},
    keywords = {ANFIS; Neuro-fuzzy; Embedded systems; Genetic Algorithms; Bacterial conjugation; Concurrency; Fixed point},
    abstract = {Abstract In this paper implementation of \{ANFIS\} on embedded systems based on single-core and multi-core \{ARM\} processors is presented. A novel evolutionary optimization tool named, modified high performance genetic algorithm (mHPGA) with bacterial conjugation operator is applied to \{ANFIS\} as a training method. Fixed point and floating point number representations are applied and compared. Moreover new mutation algorithm has been proposed for fixed point numbers. The proposed method is designed to sweep numbers space to search possible solutions in large state space. Concurrency nature of mHPGA benefits implementation of multi threading feature on \{ARM\} cortex-A53 with four cores.}
    }
  • [DOI] A. Mehrafsa, A. Sokhandan, and G. Karimian, “A timed-based approach for genetic algorithm: theory and applications,” Ieice transactions on information and systems, vol. E94-D, iss. 6, pp. 1306-1320, 2011.
    [Bibtex]
    @article{MSK2011,
    author = {Amir Mehrafsa and Alireza Sokhandan and Ghader Karimian},
    title = {A Timed-Based Approach for Genetic Algorithm: Theory and Applications},
    journal = {IEICE Transactions on Information and Systems},
    issn = {0916-8532},
    year = {2011},
    month = {June},
    volume = {E94-D},
    number = {6},
    pages = {1306-1320},
    numpages = {15},
    publisher = {Maruzen Co., Ltd.},
    language = {English},
    doi = {10.1587/transinf.E94.D.1306},
    ee = {http://search.ieice.org/bin/summary.php?id=e94-d_6_1306},
    keywords = {genetic algorithms; time unit; time to live; population; generator; crossover probability; GAVaPS; premature convergence; random search algorithm},
    abstract = {In this paper, a new algorithm called TGA is introduced which defines the concept of time more naturally for the first time. A parameter called TimeToLive is considered for each chromosome, which is a time duration in which it could participate in the process of the algorithm. This will lead to keeping the dynamism of algorithm in addition to maintaining its convergence sufficiently and stably. Thus, the TGA guarantees not to result in premature convergence or stagnation providing necessary convergence to achieve optimal answer. Moreover, the mutation operator is used more meaningfully in the TGA. Mutation probability has direct relation with parent similarity. This kind of mutation will decrease ineffective mating percent which does not make any improvement in offspring individuals and also it is more natural. Simulation results show that one run of the TGA is enough to reach the optimum answer and the TGA outperforms the standard genetic algorithm.}
    }
  • [DOI] A. Mehrafsa, A. Sokhandan, and G. Karimian, “A high performance genetic algorithm using bacterial conjugation operator (hpga),” Genetic programming and evolvable machines, vol. 14, iss. 4, pp. 395-427, 2013.
    [Bibtex]
    @article{MSK2013,
    author = {Amir Mehrafsa and Alireza Sokhandan and Ghader Karimian},
    title = {A high performance genetic algorithm using bacterial conjugation operator (HPGA)},
    journal = {Genetic Programming and Evolvable Machines},
    issn = {1389-2576},
    year = {2013},
    month = {December},
    volume = {14},
    number = {4},
    pages = {395-427},
    numpages = {33},
    publisher = {Springer US},
    language = {English},
    doi = {10.1007/s10710-013-9185-x},
    ee = {http://link.springer.com/article/10.1007_s10710-013-9185-x},
    keywords = {Evolutionary algorithm; Genetic algorithm; Bacterial conjugation; High performance; Real-time; Parameter less},
    abstract = {In this paper an efficient evolutionary algorithm is proposed which could be applied to real-time problems such as robotics applications. The only parameter of the proposed algorithm is the "Population Size" which makes the proposed algorithm similar to parameter-less algorithms, and the only operator applied during the algorithm execution is the bacterial conjugation operator, which makes using and implementation of the proposed algorithm much easier. The procedure of the bacterial conjugation operator used in this algorithm is different from operators of the same name previously used in other evolutionary algorithms such as the pseudo bacterial genetic algorithm or the microbial genetic algorithm. For a collection of 23 benchmark functions and some other well-known optimization problems, the experimental results show that the proposed algorithm has better performance when compared to particle swarm optimization and a simple genetic algorithm.}
    }
  • [DOI] A. Mehrafsa, G. Karimian, and A. Ghanbari, “A dynamic size artificial neural network for online data clustering with a new outlier handling technique,” in 16th csi international symposium on artificial intelligence and signal processing (aisp), 2012, pp. 327-332.
    [Bibtex]
    @inproceedings{MKG2012,
    author = {Amir Mehrafsa and Ghader Karimian and Ahmad Ghanbari},
    title = {A dynamic size artificial neural network for online data clustering with a new outlier handling technique},
    booktitle = {16th CSI International Symposium on Artificial Intelligence and Signal Processing (AISP)},
    isbn = {9781467314787},
    year = {2012},
    month = {May},
    pages = {327-332},
    numpages = {6},
    location = {Shiraz, Fars, Iran},
    language = {English},
    doi = {10.1109/AISP.2012.6313767},
    ee = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6313767},
    keywords = {Adaptive Resonance Theory; Artificial Neural Networks; Distribution-based Clustering; Online Data Clustering; Outlier Handling},
    abstract = {This paper presents a new online data clustering algorithm with a new outlier handling technique. The proposed algorithm procedure is based on the well-known ART networks. In recent years, ART networks have been widely used as an online data clustering technique in many applications. The problem with the ART networks is that when the network size increases due to the formation of new clusters, the clustering performance slows down. The situation will get worse if the incoming stream of data includes many outliers which will be processed by the network as new clusters. The proposed algorithm provides an online outlier handler which will solve the mentioned problem while categorizing the multi-dimensional input data using distribution-based clustering model. The outlier handling technique in the proposed algorithm could be used in other forms of ART networks such as ART1, ART2 and Fuzzy ART.}
    }
  • [DOI] A. Mehrafsa, A. Sokhandan, A. Ghanbari, and V. Azimirad, “A multi-step genetic algorithm to solve the inverse kinematics problem of the redundant open chain manipulators,” in 2nd international conference on control, instrumentation and automation (iccia), 2011, pp. 1024-1029.
    [Bibtex]
    @inproceedings{MSGA2011,
    author = {Amir Mehrafsa and Alireza Sokhandan and Ahmad Ghanbari and Vahid Azimirad},
    title = {A multi-step Genetic Algorithm to solve the inverse kinematics problem of the redundant open chain manipulators},
    booktitle = {2nd International Conference on Control, Instrumentation and Automation (ICCIA)},
    isbn = {9781467316897},
    year = {2011},
    month = {December},
    pages = {1024-1029},
    numpages = {6},
    location = {Shiraz, Fars, Iran},
    language = {English},
    doi = {10.1109/ICCIAutom.2011.6356802},
    ee = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6356802},
    keywords = {Genetic Algorithm; Inverse kinematics problem; Open-Chain manipulators; Redundant manipulator; Smooth movement},
    abstract = {This paper presents a new algorithm regarding the inverse kinematics problem of the redundant open-chain manipulators, based on Simple Genetic Algorithm (SGA). The proposed method could be applied for any kind of manipulator configuration independent from number of joints. This method formulates the inverse kinematics problem as an optimization algorithm, solves it using the SGA in two steps and can be extended further. The advantage of splitting the procedure can be beneficial when procedures execute in parallel. At the first step, the SGA looks for successive joint values set for a given manipulator as candidate joints set, and at the second one, SGA would find the optimum joint values. Therefore, the manipulator's end-effector would be smoothly moved from an initial location to its target with minimum joints displacement while avoiding singularity. Simulation studies show that the proposed method represents an efficient approach to solve the inverse kinematics problem of open-chain manipulators with any degree of redundancy.}
    }

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