IEEE Organizations related to Memetics

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Conferences related to Memetics

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2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

FUZZ-IEEE 2021 will represent a unique meeting point for scientists and engineers, both from academia and industry, to interact and discuss the latest enhancements and innovations in the field. The topics of the conference will cover all the aspects of theory and applications of fuzzy sets, fuzzy logic and associated approaches (e.g. aggregation operators such as the Fuzzy Integral), as well as their hybridizations with other artificial and computational intelligence techniques.


2020 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)

All topics related to engineering and technology management, including applicable analytical methods and economical/social/human issues to be considered in making engineering decisions.


2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

The 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2020) will be held in Metro Toronto Convention Centre (MTCC), Toronto, Ontario, Canada. SMC 2020 is the flagship conference of the IEEE Systems, Man, and Cybernetics Society. It provides an international forum for researchers and practitioners to report most recent innovations and developments, summarize state-of-the-art, and exchange ideas and advances in all aspects of systems science and engineering, human machine systems, and cybernetics. Advances in these fields have increasing importance in the creation of intelligent environments involving technologies interacting with humans to provide an enriching experience and thereby improve quality of life. Papers related to the conference theme are solicited, including theories, methodologies, and emerging applications. Contributions to theory and practice, including but not limited to the following technical areas, are invited.


2020 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM)

The scope of the 2020 IEEE/ASME AIM includes the following topics: Actuators, Automotive Systems, Bioengineering, Data Storage Systems, Electronic Packaging, Fault Diagnosis, Human-Machine Interfaces, Industry Applications, Information Technology, Intelligent Systems, Machine Vision, Manufacturing, Micro-Electro-Mechanical Systems, Micro/Nano Technology, Modeling and Design, System Identification and Adaptive Control, Motion Control, Vibration and Noise Control, Neural and Fuzzy Control, Opto-Electronic Systems, Optomechatronics, Prototyping, Real-Time and Hardware-in-the-Loop Simulation, Robotics, Sensors, System Integration, Transportation Systems, Smart Materials and Structures, Energy Harvesting and other frontier fields.


2019 20th International Symposium on Quality Electronic Design (ISQED)

20th International Symposium on Quality Electronic Design (ISQED 2019) is the premier interdisciplinary and multidisciplinary Electronic Design conference?bridges the gap among Electronic/Semiconductor ecosystem members providing electronic design tools, integrated circuit technologies, semiconductor technology,packaging, assembly & test to achieve design quality.


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Most published Xplore authors for Memetics

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Xplore Articles related to Memetics

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V2V-Based Memetic Optimization for Improving Traffic Efficiency on Multi-Lane Roads

IEEE Intelligent Transportation Systems Magazine, None

None


A Memetic Algorithm for Selection of 3D Clustered Features with Applications in Neuroscience

2010 20th International Conference on Pattern Recognition, 2010

We propose a Memetic algorithm for feature selection in volumetric data containing spatially distributed clusters of informative features, typically encountered in neuroscience applications. The proposed method complements a conventional genetic algorithm with a local search utilizing inherent spatial relationships to efficiently identify informative feature clusters across multiple regions of the search volume. First, we demonstrate the utility of the algorithm ...


A memetic algorithm to solve the open shop scheduling problem

2015 6th International Conference on Modeling, Simulation, and Applied Optimization (ICMSAO), 2015

Open shop scheduling problem (OSSP) can basically be defined as a scheduling problem where each job has just one operation to be processed on each machine and operation sequence is free to decide. It is usually seen in facilities that produce similar product families. In this study a Memetic algorithm is proposed for solving the OSSP, and the results are ...


Guest Editorial: Special Issue on Engineering Applications of Memetic Computing

IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 2012

The seven full papers and one technical correspondence in this special issue focus on the engineering applications of memetic computing.


Application Possibilities of Artificial Neural Networks in the Construction Industry

2014 International Conference on Computational Science and Computational Intelligence, 2014

The role of logistics in the construction industry is considered as a key to better performance and customer quality. This paper proposes a method to model the connection between logistics resources dedicated to a construction project and its schedule success using artificial neural networks. Schedule success can be regarded as one of the major element of customer satisfaction. Therefore a ...


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Educational Resources on Memetics

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IEEE.tv Videos

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IEEE-USA E-Books

  • V2V-Based Memetic Optimization for Improving Traffic Efficiency on Multi-Lane Roads

    None

  • A Memetic Algorithm for Selection of 3D Clustered Features with Applications in Neuroscience

    We propose a Memetic algorithm for feature selection in volumetric data containing spatially distributed clusters of informative features, typically encountered in neuroscience applications. The proposed method complements a conventional genetic algorithm with a local search utilizing inherent spatial relationships to efficiently identify informative feature clusters across multiple regions of the search volume. First, we demonstrate the utility of the algorithm on simulated data containing informative feature clusters of varying contrast-to-noise-ratios. The Memetic algorithm identified a majority of the relevant features whereas a conventional genetic algorithm detected only a subset sufficient for fitness maximization. Second, we applied the algorithm to authentic functional magnetic resonance imaging (fMRI) brain activity data from a motor task study, where the Memetic algorithm identified expected brain regions and subsequent brain activity prediction in new individuals was accurate at an average of 76% correct classification. The proposed algorithm constitutes a novel method for efficient volumetric feature selection and is applicable in any 3D data scenario. In particular, the algorithm is a promising alternative for sensitive brain activity mapping and decoding.

  • A memetic algorithm to solve the open shop scheduling problem

    Open shop scheduling problem (OSSP) can basically be defined as a scheduling problem where each job has just one operation to be processed on each machine and operation sequence is free to decide. It is usually seen in facilities that produce similar product families. In this study a Memetic algorithm is proposed for solving the OSSP, and the results are promising.

  • Guest Editorial: Special Issue on Engineering Applications of Memetic Computing

    The seven full papers and one technical correspondence in this special issue focus on the engineering applications of memetic computing.

  • Application Possibilities of Artificial Neural Networks in the Construction Industry

    The role of logistics in the construction industry is considered as a key to better performance and customer quality. This paper proposes a method to model the connection between logistics resources dedicated to a construction project and its schedule success using artificial neural networks. Schedule success can be regarded as one of the major element of customer satisfaction. Therefore a tool with the ability of predicting it can be useful in decision support.

  • Memetic-CLA-PSO: A Hybrid Model for Optimization

    In this paper a hybrid model which is a combination of Memetic algorithm, cellular learning automata (CLA) and PSO is proposed. The proposed algorithm in addition to maintaining diversity; largely reduces the probability of getting trap in local optima. Experimental results on eight optimization problems show the superiority of the proposed algorithm.

  • Optimal Design and Control of Smart Space Structures: A Memetic Evolution Approach

    To reach global optimal performance, here the authors propose a new design approach that offers concurrent design for smart space structures. In this approach, the quantity and placement of sensors/actuators and parameters of controllers are simultaneously optimized by a memetic evolution algorithm. A solar array smart structure has been used for computational experiments and the corresponding results indicate that the proposed concurrent design can obtain better performance than the sequential one.

  • Mechanical design using mixed-integer memetic algorithm with Lagrange method

    Memetic algorithm (MA) is a hybrid evolutionary algorithm (EA) that combines genetic operators with local search procedures. With global exploration and local exploitation in search process, the memetic algorithm can find near global solutions. On the other hand, mixed-integer hybrid differential evolution (MIHDE), as an EA-based search algorithm, has been successfully applied to many mixed-integer optimization problems. In this paper, a memetic algorithm based on MIHDE, called Memetic MIHDE, is proposed to solve mixed- integer optimization problems. Furthermore, in order to handle the constrained optimization problems, a Memetic MIHDE algorithm combined with Lagrange method is developed for solving the constrained optimization problems. Finally, the proposed algorithm is applied to a mechanical design optimization problem. Experimental results show that the algorithm can obtain a better optimal solution compared with some other search algorithms. This demonstrates that the Memetic MIHDE algorithm can solve mechanical design optimization problems effectively.

  • The hybrid of PTS-CAPPR methods with MA for PAPR reduction in OFDM systems

    The hybrid of partial transmit sequences (PTS) and cascade adaptive peak power reduction (CAPPR) methods have been used to reduce peak to average power ratio (PAPR) of orthogonal frequency division multiplexing(OFDM) system. When number of sub-block increases, PAPR is getting small. However, the calculation cost of its selection must be considerably large. W e introduce memetic algorithm(MA), the total calculation cost becomes drastically reduced. The parameter for simulation used standard WLAN in IEEE 802.11a system. In simulation results, the proposed method shows the improvement performance PAPR, bit error rate (BER) and power spectrum density (PSD) of an OFDM system.

  • Multilevel thresholding image segmentation using memetic algorithm

    Memetic algorithms (MAs) are hybrid algorithms aimed to improve traditional evolutionary algorithms such as genetic algorithms (GAs). Recently, MAs have been widely used in the image processing field. This paper proposes an image segmentation approach using MA. The proposed approach employs local search as an improvement operator added to the GA in order to speed up the searching process and generate the best solutions faster. The results of the experiments that were conducted on eight different images have shown that MA converges to the solutions faster than GA. In terms of processing time, MA has recorded significant improvement ranging from 17.5% to 79.8%. The comparison results have also shown that MA always achieves the same or better quality of the segmented images.



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