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Journal articles on the topic 'Algoritmi memetici'

Author: Grafiati

Published: 10 March 2023

Last updated: 11 March 2023

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1

Nico, Nico, Novrido Charibaldi, and Yuli Fauziah. "Comparison of Memetic Algorithm and Genetic Algorithm on Nurse Picket Scheduling at Public Health Center." International Journal of Artificial Intelligence & Robotics (IJAIR) 4, no. 1 (May 30, 2022): 9–23. http://dx.doi.org/10.25139/ijair.v4i1.4323.

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One of the most significant aspects of the working world is the concept of a picket schedule. It is difficult for the scheduler to make an archive since there are frequently many issues with the picket schedule. These issues include schedule clashes, requests for leave, and trading schedules. Evolutionary algorithms have been successful in solving a wide variety of scheduling issues. Evolutionary algorithms are very susceptible to data convergence. But no one has discussed where to start from, where the data converges from making schedules using evolutionary algorithms. The best algorithms among evolutionary algorithms for scheduling are genetic algorithms and memetics algorithms. When it comes to the two algorithms, using genetic algorithms or memetics algorithms may not always offer the optimum outcomes in every situation. Therefore, it is necessary to compare the genetic algorithm and the algorithm's memetic algorithm to determine which one is suitable for the nurse picket schedule. From the results of this study, the memetic algorithm is better than the genetic algorithm in making picket schedules. The memetic algorithm with a population of 10000 and a generation of 5000 does not produce convergent data. While for the genetic algorithm, when the population is 5000 and the generation is 50, the data convergence starts. For accuracy, the memetic algorithm violates only 24 of the 124 existing constraints (80,645%). The genetic algorithm violates 27 of the 124 constraints (78,225%). The average runtime used to generate optimal data using the memetic algorithm takes 20.935592 seconds. For the genetic algorithm, it takes longer, as much as 53.951508 seconds.

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Dodu, A. Y. Erwin, Deny Wiria Nugraha, and Subkhan Dinda Putra. "Penjadwalan Tenaga Kebidanan Menggunakan Algoritma Memetika." JURNAL SISTEM INFORMASI BISNIS 8, no. 1 (April 30, 2018): 99. http://dx.doi.org/10.21456/vol8iss1pp99-106.

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The problem of midwife scheduling is one of the most frequent problems in hospitals. Midwife should be available 24 hours a day for a full week to meet the needs of the patient. Therefore, good or bad midwife scheduling result will have an impact on the quality of care on the patient and the health of the midwife on duty. The midwife scheduling process requires a lot of time, effort and good cooperation between some parties to solve this problem that is often faced by the Regional Public Hospital Undata Palu Central Sulawesi Province. This research aimed to apply Memetics algorithm to make scheduling system of midwifery staff at Regional Public Hospital Undata Palu Central Sulawesi Province that can facilitate the process of midwifery scheduling as well as to produce optimal schedule. The scheduling system created will follow the rules and policies applicable in the hospital and will also pay attention to the midwife's preferences on how to schedule them according to their habits and needs. Memetics algorithm is an optimization algorithm that combines Evolution Algorithm and Local Search method. Evolution Algorithm in Memetics Algorithm generally refers to Genetic Algorithm so that the characteristics of Memetics Algotihm are identical with Genetic Algorithm characteristics with the addition of Local Search methods. Local Search in Memetic Algorithm aims to improve the quality of an individual so it is expected to accelerate the time to get a solution.

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Handa, Hisashi. "Solving Constraint Satisfaction Problems by Memetic Algorithms Using Estimation of Distribution Algorithms." Transactions of the Japanese Society for Artificial Intelligence 19 (2004): 405–12. http://dx.doi.org/10.1527/tjsai.19.405.

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4

He Zewen, 何泽文, 庄秋实 Zhuang Qiushi, 曹惠宁 Cao Huining, and 辛煜 Xin Yu. "基于文化基因算法的透过散射介质聚焦." Laser & Optoelectronics Progress 58, no. 24 (2021): 2429001. http://dx.doi.org/10.3788/lop202158.2429001.

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5

Areibi, Shawki, and Zhen Yang. "Effective Memetic Algorithms for VLSI Design = Genetic Algorithms + Local Search + Multi-Level Clustering." Evolutionary Computation 12, no. 3 (September 2004): 327–53. http://dx.doi.org/10.1162/1063656041774947.

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Combining global and local search is a strategy used by many successful hybrid optimization approaches. Memetic Algorithms (MAs) are Evolutionary Algorithms (EAs) that apply some sort of local search to further improve the fitness of individuals in the population. Memetic Algorithms have been shown to be very effective in solving many hard combinatorial optimization problems. This paper provides a forum for identifying and exploring the key issues that affect the design and application of Memetic Algorithms. The approach combines a hierarchical design technique, Genetic Algorithms, constructive techniques and advanced local search to solve VLSI circuit layout in the form of circuit partitioning and placement. Results obtained indicate that Memetic Algorithms based on local search, clustering and good initial solutions improve solution quality on average by 35% for the VLSI circuit partitioning problem and 54% for the VLSI standard cell placement problem.

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Phan, Tuan Anh, and Anh Tuan Duong. "A FRAMEWORK FOR MEMETIC ALGORITHMS." Science and Technology Development Journal 12, no. 11 (June 15, 2009): 27–38. http://dx.doi.org/10.32508/stdj.v12i11.2309.

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Memetic algorithm, a combination of genetic algorithm with local search, is one of the most successful metaheuristics to solve complex combinatorial optimization problems. In this paper, we will introduce an object-oriented framework which allows the construction of memetic algorithms with a maximum reuse. This framework has been developed in Java using design patterns to allow its easy extension and utilization in different problem domains. Our framework has been experimented through the development of a memetic algorithm for solving set covering problems.

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Vairam, Senthil, and V. Selladurai. "Parallel Machine Shop Scheduling Using Memetic Algorithm." Applied Mechanics and Materials 573 (June 2014): 362–67. http://dx.doi.org/10.4028/www.scientific.net/amm.573.362.

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Parallel machine shop scheduling problem can be stated as finding a schedule for a general task graph to execute on a customed flow so that the schedule length can be minimized. Parallel Flow Shop Scheduling with a case study has been . In this study we present an effective memetic algorithm to solve the problem. Also evaluating the performance of two algorithms (genetic algorithm and memetic algorithm) in terms of both the quality of the solutions produced and the efficiency. These results demonstrate that the memetic algorithm produces better and quality solutions and hence it is very efficient . KEY WORDS: Hybrid Flow Shop Scheduling, Multiprocessor, Memetic algorithm.

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Bharothu, Dr Jyothilal Nayak, Dr B. Madhu Kiran, Dr G. Kishor Babu, and B. N. V. Satish Kumar Kolla. "IEEE -30 Bus System Study with Memetic Differential Evolution Algorithm." Journal of Advanced Research in Dynamical and Control Systems 11, no. 11 (November 20, 2019): 86–96. http://dx.doi.org/10.5373/jardcs/v11i11/20193172.

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Lozano, Manuel, Francisco Herrera, Natalio Krasnogor, and Daniel Molina. "Real-Coded Memetic Algorithms with Crossover Hill-Climbing." Evolutionary Computation 12, no. 3 (September 2004): 273–302. http://dx.doi.org/10.1162/1063656041774983.

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This paper presents a real-coded memetic algorithm that applies a crossover hill-climbing to solutions produced by the genetic operators. On the one hand, the memetic algorithm provides global search (reliability) by means of the promotion of high levels of population diversity. On the other, the crossover hill-climbing exploits the self-adaptive capacity of real-parameter crossover operators with the aim of producing an effective local tuning on the solutions (accuracy). An important aspect of the memetic algorithm proposed is that it adaptively assigns different local search probabilities to individuals. It was observed that the algorithm adjusts the global/local search balance according to the particularities of each problem instance. Experimental results show that, for a wide range of problems, the method we propose here consistently outperforms other real-coded memetic algorithms which appeared in the literature.

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Elleuch, Souhir, and Bassem Jarboui. "Improved memetic programming algorithm." International Journal of Operational Research 44, no. 3 (2022): 389. http://dx.doi.org/10.1504/ijor.2022.124105.

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Huy, Nguyen Quang, Ong Yew Soon, Lim Meng Hiot, and Natalio Krasnogor. "Adaptive Cellular Memetic Algorithms." Evolutionary Computation 17, no. 2 (June 2009): 231–56. http://dx.doi.org/10.1162/evco.2009.17.2.231.

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A cellular genetic algorithm (CGA) is a decentralized form of GA where individuals in a population are usually arranged in a 2D grid and interactions among individuals are restricted to a set neighborhood. In this paper, we extend the notion of cellularity to memetic algorithms (MA), a configuration termed cellular memetic algorithm (CMA). In addition, we propose adaptive mechanisms that tailor the amount of exploration versus exploitation of local solutions carried out by the CMA. We systematically benchmark this adaptive mechanism and provide evidence that the resulting adaptive CMA outperforms other methods both in the quality of solutions obtained and the number of function evaluations for a range of continuous optimization problems.

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Elleuch, Souhir, and Bassem Jarboui. "Improved Memetic Programming algorithm." International Journal of Operational Research 1, no. 1 (2021): 1. http://dx.doi.org/10.1504/ijor.2021.10032941.

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Vakil-Baghmisheh, M. T., and Morteza Alinia Ahandani. "A differential memetic algorithm." Artificial Intelligence Review 41, no. 1 (January 1, 2012): 129–46. http://dx.doi.org/10.1007/s10462-011-9302-2.

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Molina, Daniel, Manuel Lozano, Carlos García-Martínez, and Francisco Herrera. "Memetic Algorithms for Continuous Optimisation Based on Local Search Chains." Evolutionary Computation 18, no. 1 (March 2010): 27–63. http://dx.doi.org/10.1162/evco.2010.18.1.18102.

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Memetic algorithms with continuous local search methods have arisen as effective tools to address the difficulty of obtaining reliable solutions of high precision for complex continuous optimisation problems. There exists a group of continuous local search algorithms that stand out as exceptional local search optimisers. However, on some occasions, they may become very expensive, because of the way they exploit local information to guide the search process. In this paper, they are called intensive continuous local search methods. Given the potential of this type of local optimisation methods, it is interesting to build prospective memetic algorithm models with them. This paper presents the concept of local search chain as a springboard to design memetic algorithm approaches that can effectively use intense continuous local search methods as local search operators. Local search chain concerns the idea that, at one stage, the local search operator may continue the operation of a previous invocation, starting from the final configuration (initial solution, strategy parameter values, internal variables, etc.) reached by this one. The proposed memetic algorithm favours the formation of local search chains during the memetic algorithm run with the aim of concentrating local tuning in search regions showing promise. In order to study the performance of the new memetic algorithm model, an instance is implemented with CMA-ES as an intense local search method. The benefits of the proposal in comparison to other kinds of memetic algorithms and evolutionary algorithms proposed in the literature to deal with continuous optimisation problems are experimentally shown. Concretely, the empirical study reveals a clear superiority when tackling high-dimensional problems.

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Ammaruekarat, Paranya, and Phayung Meesad. "Multi-Objective Chaos Memetic Algorithm for DTLZ Problems." Advanced Materials Research 403-408 (November 2011): 3676–81. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.3676.

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Based on Multi-Objective Memetic Algorithm (MOMA), a novel Multi-Objective Chaos Memetic Algorithm (MOCMA) is proposed . MOCMA is presented to keep population’s diversity, avoid local optimum and improve performance of Multi-Objective Memetic Algorithm. By virtue of the over-spread character of chaos sequence, it is used to generate chromosome to overcome redundancies. At the same time, searching space is enlarged by using sensitivity of chaos initial value. The comparisons of MOCMA with NSGAII in DTLZ problems suggest that MOCMA clearly outperforms in converging towards the true pareto front and finding the spread of solutions.

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Wang, Longda, Xingcheng Wang, and Gang Liu. "An Improved Force Characteristic Curve Fitting Algorithm of Urban Rail Vehicles." Journal of Sensors 2022 (April 1, 2022): 1–13. http://dx.doi.org/10.1155/2022/9910982.

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In this paper, an improved force characteristic curve fitting memetic algorithm of urban rail vehicles is proposed for establishing precise train operation models. In order to improve the memetic algorithm global convergence, three strategies are adopted. In the improved memetic algorithm framework, an improved moth-flame optimization is used in global search; an improved simulated annealing is applied in local search; a new learning mechanism incorporated into reverse learning is adopted. Experimental simulation results under real-time data monitoring system show that the improved memetic algorithm proposed in this paper can increase the optimization performance effectively so more perfect force characteristic curve fitting effort can be obtained, and the calculated average force error and max running distance error can be reduced effectively. Moreover, the above relative results indicate that the train energy consumption model using the improved force characteristic curve fitting algorithm can obtain more precise energy consumption. Obviously, the improved force characteristic curve fitting algorithm can effectively improve the curve fitting precision.

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Engin, Orhan, and Batuhan Engin. "Hybrid flow shop with multiprocessor task scheduling based on earliness and tardiness penalties." Journal of Enterprise Information Management 31, no. 6 (October 8, 2018): 925–36. http://dx.doi.org/10.1108/jeim-04-2017-0051.

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Purpose Hybrid flow shop with multiprocessor task (HFSMT) has received considerable attention in recent years. The purpose of this paper is to consider an HFSMT scheduling under the environment of a common time window. The window size and location are considered to be given parameters. The research deals with the criterion of total penalty cost minimization incurred by earliness and tardiness of jobs. In this research, a new memetic algorithm in which a global search algorithm is accompanied with the local search mechanism is developed to solve the HFSMT with jobs having a common time window. The operating parameters of memetic algorithm have an important role on the quality of solution. In this paper, a full factorial experimental design is used to determining the best parameters of memetic algorithm for each problem type. Memetic algorithm is tested using HFSMT problems. Design/methodology/approach First, hybrid flow shop scheduling system and hybrid flow shop scheduling with multiprocessor task are defined. The applications of the hybrid flow shop system are explained. Also the background of hybrid flow shop with multiprocessor is given in the introduction. The features of the proposed memetic algorithm are described in Section 2. The experiment results are presented in Section 3. Findings Computational experiments show that the proposed new memetic algorithm is an effective and efficient approach for solving the HFSMT under the environment of a common time window. Originality/value There is only one study about HFSMT scheduling with time window. This is the first study which added the windows to the jobs in HFSMT problems.

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Neri, Ferrante, and Carlos Cotta. "Memetic algorithms and memetic computing optimization: A literature review." Swarm and Evolutionary Computation 2 (February 2012): 1–14. http://dx.doi.org/10.1016/j.swevo.2011.11.003.

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Yoon, Yourim, and Zong Woo Geem. "Parameter Optimization of Single-Diode Model of Photovoltaic Cell Using Memetic Algorithm." International Journal of Photoenergy 2015 (2015): 1–7. http://dx.doi.org/10.1155/2015/963562.

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This study proposes a memetic approach for optimally determining the parameter values of single-diode-equivalent solar cell model. The memetic algorithm, which combines metaheuristic and gradient-based techniques, has the merit of good performance in both global and local searches. First, 10 single algorithms were considered including genetic algorithm, simulated annealing, particle swarm optimization, harmony search, differential evolution, cuckoo search, least squares method, and pattern search; then their final solutions were used as initial vectors for generalized reduced gradient technique. From this memetic approach, we could further improve the accuracy of the estimated solar cell parameters when compared with single algorithm approaches.

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20

Kaweegitbundit, Parinya. "Comparison of Heuristic for Flow Shop Scheduling Problems with Sequence Dependent Setup Time." Advanced Materials Research 339 (September 2011): 332–35. http://dx.doi.org/10.4028/www.scientific.net/amr.339.332.

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This paper considers flow shop scheduling problems with sequence dependent setup time. The makespan criterion has been considered. In this paper presented a comparison of three heuristics for solves this problem. The memetic algorithm, genetic algorithm and NEH heuristic have been compared. In the experimental, the result from memetic algorithm is maximum the best solution. Therefore, the MA heuristic outperforms other heuristic.

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Mahdi, Samir, and Brahim Nini. "Improved Memetic NSGA-II Using a Deep Neighborhood Search." International Journal of Applied Metaheuristic Computing 12, no. 4 (October 2021): 138–54. http://dx.doi.org/10.4018/ijamc.2021100108.

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Elitist non-sorted genetic algorithms as part of Pareto-based multi-objective evolutionary algorithms seems to be one of the most efficient algorithms for multi-objective optimization. However, it has some shortcomings, such as low convergence accuracy, uneven Pareto front distribution, and slow convergence. A number of review papers using memetic technique to improve NSGA-II have been published. Hence, it is imperative to improve memetic NSGA-II by increasing its solving accuracy. In this paper, an improved memetic NSGA-II, called deep memetic non-sorted genetic algorithm (DM-NSGA-II), is proposed, aiming to obtain more non-dominated solutions uniformly distributed and better converged near the true Pareto-optimal front. The proposed algorithm combines the advantages of both exact and heuristic approaches. The effectiveness of DM-NSGA-II is validated using well-known instances taken from the standard literature on multi-objective knapsack problem. As will be shown, the performance of the proposed algorithm is demonstrated by comparing it with M-NSGA-II using hypervolume metric.

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Godinho, Pedro, Luiz Moutinho, and Margherita Pagani. "A memetic algorithm for maximizing earned attention in social media." Journal of Modelling in Management 12, no. 3 (August 14, 2017): 364–85. http://dx.doi.org/10.1108/jm2-10-2015-0078.

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Purpose The purpose of this study is to propose a measure for earned attention and a model and procedure for the maximization of earned attention by a company over a period of time. Design/methodology/approach Utility functions are used as the base of the earned attention measure. An evolutionary algorithm – a memetic algorithm – is applied to identify strategies that aim to maximize earned attention. Computational analysis is performed resorting to simulated data, and the memetic algorithm is assessed through the comparison with a standard steepest ascent heuristic. Findings The shape of the utility functions considered in the model has a huge impact on the characteristics of the best strategies, with actions focused on increasing a single variable being preferred in case of constant marginal utility, and more balanced strategies having a better performance in the case of decreasing marginal utility. The memetic algorithm is shown to have a much better performance that the steepest ascent procedure. Originality/value A new mathematical model for earned attention is proposed, and an approach that has few applications in business problems – a memetic algorithm – is tailored to the model and applied to identify solutions.

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Pu, Huangzhong, Ziyang Zhen, and Daobo Wang. "Modified shuffled frog leaping algorithm for optimization of UAV flight controller." International Journal of Intelligent Computing and Cybernetics 4, no. 1 (March 29, 2011): 25–39. http://dx.doi.org/10.1108/17563781111115778.

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PurposeAttitude control of unmanned aerial vehicle (UAV) is the purposeful manipulation of controllable external forces to establish a desired attitude, which is inner‐loop of the autonomous flight control system. In the practical applications, classical control methods such as proportional‐integral‐derivative control are usually selected because of simple and high reliability. However, it is usually difficult to select or optimize the control parameters. The purpose of this paper is to investigate an intelligent algorithm based classical controller of UAV.Design/methodology/approachAmong the many intelligent algorithms, shuffled frog leaping algorithm (SFLA) combines the benefits of the genetic‐based memetic algorithm as well as social behavior based particle swarm optimization. SFLA is a population based meta‐heuristic intelligent optimization method inspired by natural memetics. In order to improve the performance of SFLA, a different dividing method of the memeplexes is presented to make their performance balance; moreover, an evolution mechanism of the best frog is introduced to make the algorithm jump out the local optimum. The modified SFLA is applied to the tuning of the proportional coefficients of pitching and rolling channels of UAV flight control system.FindingsSimulation of a UAV control system in which the nonlinear model is obtained by the wind tunnel experiment show the rapid dynamic response and high control precision by using the modified SFLA optimized attitude controller, which is better than that of the original SFLA and particle swarm optimization method.Originality/valueA modification scheme is presented to improve the global searching capability of SFLA. The modified SFLA based intelligent determination method of the UAV flight controller parameters is proposed, in order to improve the attitude control performance of UAV.

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Zhang, Guohui, Jinghe Sun, Xixi Lu, and Haijun Zhang. "An improved memetic algorithm for the flexible job shop scheduling problem with transportation times." Measurement and Control 53, no. 7-8 (August 2020): 1518–28. http://dx.doi.org/10.1177/0020294020948094.

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In the practical production, the transportation of jobs is existed between different machines. These transportation operations directly affect the production cycle and the production efficiency. In this study, an improved memetic algorithm is proposed to solve the flexible job shop scheduling problem with transportation times, and the optimization objective is minimizing the makespan. In the improved memetic algorithm, an effective simulated annealing algorithm is adopted in the local search process, which combines the elite library and mutation operation. All the feasible solutions are divided into general solutions and local optimal solutions according to the elite library. The general solutions are executed by the simulated annealing algorithm to improve the quality, and the local optimal solutions are executed by the mutation operation to increase the diversity of the solution set. Comparison experiments with the improved genetic algorithm show that the improved memetic algorithm has better search performance and stability.

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Lin, Xianghong, Tingyu Ren, Jie Yang, and Xiangwen Wang. "Multi-objective cellular memetic algorithm." International Journal of Computing Science and Mathematics 15, no. 3 (2022): 213. http://dx.doi.org/10.1504/ijcsm.2022.124723.

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Wang, Xiangwen, Jie Yang, Tingyu Ren, and Xianghong Lin. "Multi-objective cellular memetic algorithm." International Journal of Computing Science and Mathematics 15, no. 3 (2022): 213. http://dx.doi.org/10.1504/ijcsm.2022.10049412.

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Sheng, Weiguo, Gareth Howells, Michael Fairhurst, and Farzin Deravi. "A Memetic Fingerprint Matching Algorithm." IEEE Transactions on Information Forensics and Security 2, no. 3 (September 2007): 402–12. http://dx.doi.org/10.1109/tifs.2007.902681.

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Ong, Yew-Soon, Natalio Krasnogor, and Hisao Ishibuchi. "Special Issue on Memetic Algorithms." IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) 37, no. 1 (February 2007): 2–5. http://dx.doi.org/10.1109/tsmcb.2006.883274.

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Badillo, Ana Reyes, Juan Jesús Ruiz, Carlos Cotta, and Antonio J. Fernández-Leiva. "On user-centric memetic algorithms." Soft Computing 17, no. 2 (July 17, 2012): 285–300. http://dx.doi.org/10.1007/s00500-012-0893-6.

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Digalakis, J., and K. Margaritis. "Performance comparison of memetic algorithms." Applied Mathematics and Computation 158, no. 1 (October 2004): 237–52. http://dx.doi.org/10.1016/j.amc.2003.08.115.

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Zhang, Guanghui, Wenjing Ma, Keyi Xing, Lining Xing, and Kesheng Wang. "Quantum-Inspired Distributed Memetic Algorithm." Complex System Modeling and Simulation 2, no. 4 (December 2022): 334–53. http://dx.doi.org/10.23919/csms.2022.0021.

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Zhang, Liping, Xinyu Li, Long Wen, and Guohui Zhang. "An Efficient Memetic Algorithm for Dynamic Flexible Job Shop Scheduling with Random Job Arrivals." International Journal of Software Science and Computational Intelligence 5, no. 1 (January 2013): 63–77. http://dx.doi.org/10.4018/ijssci.2013010105.

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Much of the research on flexible job shop scheduling problem has ignored dynamic events in dynamic environment where there are complex constraints and a variety of unexpected disruptions. This paper proposes an efficient memetic algorithm to solve the flexible job shop scheduling problem with random job arrivals. Firstly, a periodic policy is presented to update the problem condition and generate the rescheduling point. Secondly, the efficient memetic algorithm with a new local search procedure is proposed to optimize the problem at each rescheduling point. Five kinds of neighborhood structures are presented in the local search. Moreover, the performance measures investigated respectively are: minimization of the makespan and minimization of the mean tardiness. Finally, several experiments have been designed to test and evaluated the performance of the memetic algorithm. The experimental results show that the proposed algorithm is efficient to solve the flexible job shop scheduling problem in dynamic environment.

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Nguyen, Phan Trung Hai, and Dirk Sudholt. "Memetic algorithms outperform evolutionary algorithms in multimodal optimisation." Artificial Intelligence 287 (October 2020): 103345. http://dx.doi.org/10.1016/j.artint.2020.103345.

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Nogueras, Rafael, and Carlos Cotta. "Analyzing self-★ island-based memetic algorithms in heterogeneous unstable environments." International Journal of High Performance Computing Applications 32, no. 5 (December 1, 2016): 676–92. http://dx.doi.org/10.1177/1094342016678665.

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Computational environments emerging from the pervasiveness of networked devices offer a plethora of opportunities and challenges. The latter arise from their dynamic, inherently volatile nature that tests the resilience of algorithms running on them. Here we consider the deployment of population-based optimization algorithms on such environments, using the island model of memetic algorithms for this purpose. These memetic algorithms are endowed with self-★ properties that give them the ability to work autonomously in order to optimize their performance and to react to the instability of computational resources. The main focus of this work is analyzing the performance of these memetic algorithms when the underlying computational substrate is not only volatile but also heterogeneous in terms of the computational power of each of its constituent nodes. To this end, we use a simulated environment that allows experimenting with different volatility rates and heterogeneity scenarios (that is, different distributions of computational power among computing nodes), and we study different strategies for distributing the search among nodes. We observe that the addition of self-scaling and self-healing properties makes the memetic algorithm very robust to both system instability and computational heterogeneity. Additionally, a strategy based on distributing single islands on each computational node is shown to perform globally better than placing many such islands on each of them (either proportionally to their computing power or subject to an intermediate compromise).

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Zhou, Yu Yu, Yun Qing Rao, Guo Jun Zhang, and Chao Yong Zhang. "An Adaptive Memetic Algorithm for Packing Problems of Irregular Shapes." Advanced Materials Research 314-316 (August 2011): 1029–33. http://dx.doi.org/10.4028/www.scientific.net/amr.314-316.1029.

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Packing problem, which occurs frequently in sheet metal, clothing and furniture industry, cut product patterns from raw materials most efficiently and maximize material utilization. In this paper, an adaptive memetic algorithm is proposed to solve the problem of irregular shapes packed on the rectangular sheets. First, operators and parameters of evolution is researched, and second, local search method is proposed. Finally, this study compares benchmarks presented by other authors. The results show that the material utilization efficiency by using the adaptive memetic algorithm is higher compared to other methods.

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Kumar, Sandeep, Vivek Kumar Sharma, and Rajani Kumari. "Memetic Search in Differential Evolution Algorithm." International Journal of Computer Applications 90, no. 6 (March 1, 2014): 40–47. http://dx.doi.org/10.5120/15582-4406.

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QI, Yu-Tao, Fang LIU, Wei-Yuan CHANG, Xiao-Liang MA, and Li-Cheng JIAO. "Memetic Immune Algorithm for Multiobjective Optimization." Journal of Software 24, no. 7 (January 16, 2014): 1529–44. http://dx.doi.org/10.3724/sp.j.1001.2013.04282.

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Krasnogor, Natalio. "An unorthodox introduction to Memetic Algorithms." ACM SIGEVOlution 3, no. 4 (December 31, 2008): 6–15. http://dx.doi.org/10.1145/1621943.1621945.

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Carneiro, Milena Bueno P., Antônio Cláudio P. Veiga, Fernando C. de Castro, Edna Lúcia Flôres, and Gilberto A. Carrijo. "LOCALIZING THE IRIS THROUGH MEMETIC ALGORITHMS." Applied Artificial Intelligence 23, no. 8 (October 16, 2009): 738–57. http://dx.doi.org/10.1080/08839510903208070.

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Korosec, Peter, Gregor Papa, and Vida Vukasinovic. "Production scheduling with a memetic algorithm." International Journal of Innovative Computing and Applications 2, no. 4 (2010): 244. http://dx.doi.org/10.1504/ijica.2010.036812.

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Ong, Y. S., and A. J. Keane. "Meta-Lamarckian Learning in Memetic Algorithms." IEEE Transactions on Evolutionary Computation 8, no. 2 (April 2004): 99–110. http://dx.doi.org/10.1109/tevc.2003.819944.

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Tang, Maolin, and Xin Yao. "A Memetic Algorithm for VLSI Floorplanning." IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) 37, no. 1 (February 2007): 62–69. http://dx.doi.org/10.1109/tsmcb.2006.883268.

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Tong, Guowei, Hongyan Chen, and Shi Liu. "Memetic reconstruction algorithm for the ECT." IET Science, Measurement & Technology 12, no. 7 (October 1, 2018): 917–24. http://dx.doi.org/10.1049/iet-smt.2018.5241.

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Fernández-Leiva, Antonio J., and Álvaro Gutiérrez-Fuentes. "On distributed user-centric memetic algorithms." Soft Computing 23, no. 12 (February 3, 2018): 4019–39. http://dx.doi.org/10.1007/s00500-018-3049-5.

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Rezapoor Mirsaleh, M., and M. R. Meybodi. "A learning automata-based memetic algorithm." Genetic Programming and Evolvable Machines 16, no. 4 (January 21, 2015): 399–453. http://dx.doi.org/10.1007/s10710-015-9241-9.

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Özcan, Ender, and Esin Onbaşioğlu. "Memetic Algorithms for Parallel Code Optimization." International Journal of Parallel Programming 35, no. 1 (December 2, 2006): 33–61. http://dx.doi.org/10.1007/s10766-006-0026-x.

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Lü, Zhipeng, and Jin-Kao Hao. "A memetic algorithm for graph coloring." European Journal of Operational Research 203, no. 1 (May 2010): 241–50. http://dx.doi.org/10.1016/j.ejor.2009.07.016.

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Mashwani, Wali Khan, and Abdellah Salhi. "Multiobjective memetic algorithm based on decomposition." Applied Soft Computing 21 (August 2014): 221–43. http://dx.doi.org/10.1016/j.asoc.2014.03.007.

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Amen, AL-Khafaji. "Memetic Algorithm for Dynamic Optimization Problems." International Journal of Computer Applications 136, no. 3 (February 17, 2016): 7–10. http://dx.doi.org/10.5120/ijca2016908393.

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Sun, Jing, Zhuang Miao, Dunwei Gong, Xiao-Jun Zeng, Junqing Li, and Gaige Wang. "Interval Multiobjective Optimization With Memetic Algorithms." IEEE Transactions on Cybernetics 50, no. 8 (August 2020): 3444–57. http://dx.doi.org/10.1109/tcyb.2019.2908485.

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