Minh Nghia Le · 2012
To date, most successful advanced stochastic optimization algorithms involve some forms of individual learning or meme in their design. Memetic Algorithm (MA), as a form of hybridization between population-based and individual-based searches, represents one of the recent growing areas in evolutionary algorithm research. In the success and surge in interests on MAs, researchers have been exploring on various possible hybridizations of search operators towards the development and manual crafting of specialized algorithms that solve a specific problem or a set of problems effectively, using the domain knowledge obtained from human expertise. However, with so many population-based and individual-based procedures available for hybridizing, it is a tedious task, if not impossible, to design in advance an effective memetic algorithm for a given problem at hand. Furthermore, when high-fidelity analysis codes are used I would like to express my deepest gratitude to my academic supervisor in Nanyang Technological University (NTU), Dr. Ong Yew Soon.
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Rahab M. Ramadan (4915474), Safa M. Gasser (4915471), Mohamed S. El-Mahallawy (4915465) et al. · Figshare · 2018
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Mohammad-Taghi Vakil-Baghmisheh, Morteza Alinia Ahandani · Artificial Intelligence Review · 2011
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Vu Manh Xuan, Nguyen Thanh Thuy · 2010
Abstract. One of the important properties of evolutionary algorithms is to keep the diversity of the population. This paper presents an algorithm which can be regarded as the integration between Genetic Algorithm (GA) and Evolutionary Strategy (ES). This algorithm has many good properties, especially it satisfies the above important property. 1.
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Souhir Elleuch, Bassem Jarboui · International Journal of Operational Research · 2020
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Communications in Mathematical Biology and Neuroscience · 2021
A high-performance computation (HPC) is a very needed thing in the organization, researcher, or government to increase innovation of the products and services. This term belongs to parallel computation that can be realized by utilizing multi-threads of the processor. Memetic algorithm (MA) is a simple and powerful algorithm that can run parallel. In this work, we use the original memetic algorithm and a parallel memetic algorithm, so-called a high-performance memetic algorithm (HPCMA) for fingerprint identification, one of the biometric features in the human body. To generate a high-quality biometric identification system, we should pay attention to accuracy, speed up, endurance, and acceptance uses reasonable resources, and reliable to prevent criminalities.
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Nekuri Naveen, Mamillapalli Chilaka Rao · Lecture notes in computer science · 2016
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Weiguo Sheng, Gareth Howells, Michael Fairhurst et al. · IEEE Transactions on Information Forensics and Security · 2007
Minutiae point pattern matching is the most common approach for fingerprint verification. Although many minutiae point pattern matching algorithms have been proposed, reliable automatic fingerprint verification remains as a challenging problem, both with respect to recovering the optimal alignment and the construction of an adequate matching function. In this paper, we develop a memetic fingerprint matching algorithm (MFMA) which aims to identify the optimal or near optimal global matching between two minutiae sets. Within the MFMA, we first introduce an efficient matching operation to produce an initial population of local alignment configurations by examining local features of minutiae. Then, we devise a hybrid evolutionary procedure by combining the use of the global search functionality of a genetic algorithm with a local improvement operator to search for the optimal or near optimal global alignment. Finally, we define a reliable matching function for fitness computation. The proposed algorithm was evaluated by means of a series of experiments conducted on the FVC2002 database and compared with previous work. Experimental results confirm that the MFMA is an effective and practical matching algorithm for fingerprint verification. The algorithm is faster and more accurate than a traditional genetic-algorithm-based method. It is also more accurate than a number of other methods implemented for comparison, though our method generally requires more computational time in performing fingerprint matching.
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Sriperambuduri Vinay Kumar, Pembarthi Deekshitha, Varaganti Varsha · Zenodo (CERN European Organization for Nuclear Research) · 2025
Cloud computing has provided a new prospect formodern data processing because it enables scaling, elasticity, andeconomical management of computing resources for differentservices. One of the major problems in this area is efficientworkflow scheduling, which comprises of allocating severalinteracting subtasks to virtual resources in a manner that gainsoptimization. This work proposes an advanced schedule based ona Memetic Algorithm which is an hybrid algorithm includingGenetic Algorithm, robust global explorations and more effectivesolutions through precise optimization with local Hill Climbing.This hybrid strategy enhances efficiency in both the pace andquality of results obtained. Such improvements resolve the cloudworkflow scheduling problems intrinsic to its NP hardproperties. It also utilizes dynamic resource allocation systemswhich aim for reduced execution costs and makespan bydynamically aligning tasks to the distributed resources offered inthe cloud. Simulations using Cloud Sim showed that responsetime, cost, and resource utilization were significantly improvedwhen compared to Genetic Algorithm. Despite of havingchallenges such as computational demands and scalability inlarge scale environments this approach marks a significantadvancement in cloud workflow scheduling, showcasing greatpotential for future improvements
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Václav Hrbek, Tomáš Brandejský · Lecture notes in networks and systems · 2023
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Tri A. Budiono, Kok Wai Wong · 2015
This is the author’s final version of the work, as accepted for publication following peer review but without the publisher’s layout or pagination. The definitive version is available at
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Anh-Duc Do, Siu-Yeung Cho · 2007
This paper presents a Memetic Algorithm (MA) based Fuzzy C-Means (FCM) clustering algorithm. Traditional FCM algorithm suffers from the problem of local optimal, whereas the proposed MA-based FCM algorithm is able to overcome this problem and produce good performance in various ways. Experimental results showed that the proposed clustering algorithm outperforms traditional fuzzy clustering algorithms significantly on a wide variety of datasets with overlapping class boundaries and spread data distributions.
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Iztok Fister, Xin‐She Yang, Janez Brest et al. · Elsevier eBooks · 2013
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Zhiwei Zeng, Xiao-Min Hu, Min Li et al. · Proceedings of the Genetic and Evolutionary Computation Conference Companion · 2018
he research in capacitated arc routing problems (CARPs) is getting more and more attention for its wide applications in reality. Memetic algorithms (MAs) are promising in solving CARPs, but the intensity of local search (LS) in MAs should adapt to the evolution of the population for achieving the maximal effectiveness. In this paper, a novel MA based on adaptive adjustment of LS intensity (LIMA) is proposed. LIMA dynamically adjusts the probability of LS according to the distance between the individual of the population and the solution lower bound for further deepening the LS depth of potential solutions. It makes the algorithm spend as much time as possible on more potential solutions, so as to shorten the computation time and speed up the convergence. Experimental results show that LIMA has faster convergence speed and better global search ability than traditional MAs. Adaptively tuning the LS intensity of a MA has high potential for solving complex problems.1
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International Journal of Advance Engineering and Research Development · 2014
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Duan, Van Khanh. · DR-NTU (Nanyang Technological University) · 2009
Due to economic crisis, some hedge funds and investment banks face the prospect of bankruptcy, including those that are well managed. This brings about the idea of portfolio management, an area extensively discussed in the journals and economic papers. In this project, we focus on using genetic/ memetic algorithm and Markowitz’s theory of modern portfolio to help investors manage their portfolio effectively. In short, genetic/ memetic algorithm is a search technique used in computing to find approximate solutions to optimization and search problems. This project also research about game to measure level of risk aversion of investors by providing a virtual market trading platform which is relatively new in the field of finance.
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Jing Tang, et al. · 2006
In this paper, we propose the island model parallel memetic algorithm with diversity-based dynamic adaptive strategy (PMA-DLS) for controlling the local search frequency and demonstrate its utility in solving complex combinatorial optimization problems, in particular large-scale quadratic assignment problems (QAPs). The empirical results show that PMA-DLS converges to competitive solutions at significantly lower computational cost when compared to the canonical MA and PMA. Furthermore, compared to our previous work on PMA using static adaptation strategy, it is found that the diversity-based dynamic adaptation strategy displays better robustness in terms of solution quality across the class of QAP problems considered without requiring extra effort in selecting suitable parameters.
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Optimization Problems Jason, Jason Digalakis, Konstantinos Margaritis · 2001
this paper we examine 10 di#erent functions in order (a) to test specific parameter of a parallel execution of memetic algorithms and (b) to evaluate the general computational behavior of MAs. The available theoretical analysis on memetic algorithms does not o#er a tool which could help in a generalized adjustment of control parameters, leaving the choice of the proper operators, parameters and mechanisms to depend on the problem's demands, and the experience and preferences of the researcher
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Maolin Tang, Xin Yao · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · 2007
Floorplanning is an important problem in very large scale integrated-circuit (VLSI) design automation as it determines the performance, size, yield, and reliability of VLSI chips. From the computational point of view, VLSI floorplanning is an NP-hard problem. In this paper, a memetic algorithm (MA) for a nonslicing and hard-module VLSI floorplanning problem is presented. This MA is a hybrid genetic algorithm that uses an effective genetic search method to explore the search space and an efficient local search method to exploit information in the search region. The exploration and exploitation are balanced by a novel bias search strategy. The MA has been implemented and tested on popular benchmark problems. Experimental results show that the MA can quickly produce optimal or nearly optimal solutions for all the tested benchmark problems.
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Dan Qaurooni · 2011
Course timetabling consists in scheduling a sequence of lectures while satisfying various constraints. In this paper, we develop and study the performance of a memetic algorithm, designed to solve a variant of the course timetabling problem. Our aim here is twofold: to develop a competitive algorithm, and to investigate, more generally, the applicability of evolutionary algorithms to timetabling. To this end, an algorithm is first introduced and tested using a benchmark set. Comparison with other algorithms shows that our algorithm achieves better results in some, but not all instances, signifying strong and weak points. Subsequently, more comprehensive analyses are performed in relation with another evolutionary algorithm that uses strictly group-based operators. Ultimately, empirical results and analyses lead us to question the exclusive use of group-based evolutionary operators for timetabling problems.
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