Relevant bibliographies by topics / Algoritmi memetici

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Algoritmi memetici'

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Published: 10 March 2023
Last updated: 11 March 2023

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Journal articles on the topic "Algoritmi memetici"

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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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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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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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Dissertations / Theses on the topic "Algoritmi memetici"

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Vitiello, Autilia. "Memetic algorithms for ontology alignment." Doctoral thesis, Universita degli studi di Salerno, 2013. http://hdl.handle.net/10556/1156.

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2011 - 2012
Semantic interoperability represents the capability of two or more systems to meaningfully and accurately interpret the exchanged data so as to produce useful results. It is an essential feature of all distributed and open knowledge based systems designed for both e-government and private businesses, since it enables machine interpretation, inferencing and computable logic. Unfortunately, the task of achieving semantic interoperability is very difficult because it requires that the meanings of any data must be specified in an appropriate detail in order to resolve any potential ambiguity. Currently, the best technology recognized for achieving such level of precision in specification of meaning is represented by ontologies. According to the most frequently referenced definition [1], an ontology is an explicit specification of a conceptualization, i.e., the formal specification of the objects, concepts, and other entities that are presumed to exist in some area of interest and the relationships that hold them [2]. However, different tasks or different points of view lead ontology designers to produce different conceptualizations of the same domain of interest. This means that the subjectivity of the ontology modeling results in the creation of heterogeneous ontologies characterized by terminological and conceptual discrepancies. Examples of these discrepancies are the use of different words to name the same concept, the use of the same word to name different concepts, the creation of hierarchies for a specific domain region with different levels of detail and so on. The arising so-called semantic heterogeneity problem represents, in turn, an obstacle for achieving semantic interoperability... [edited by author]
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Maciel, Cristiano Baptista Faria. "A memetic algorithm for logistics network design problems." Master's thesis, Instituto Superior de Economia e Gestão, 2014. http://hdl.handle.net/10400.5/8601.

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Mestrado em Decisão Económica e Empresarial
Neste trabalho, um algoritmo memético é desenvolvido com o intuito de ser aplicado a uma rede logística, com três níveis, múltiplos períodos, seleção do meio de transporte e com recurso a outsourcing. O algoritmo memético pode ser aplicado a uma rede logística existente, no sentido de otimizar a sua configuração ou, se necessário, pode ser utilizado para criar uma rede logística de raiz. A produção pode ser internalizada e é permitido o envio direto de produtos para os clientes. Neste problema, as capacidades das diferentes infraestruturas podem ser expandidas ao longo do período temporal. Caso se trate uma infraestrutura já existente, após uma expansão, já não pode ser encerrada. Sempre que se abre uma nova infraestrutura, a mesma também não pode ser encerrada. A heurística é capaz de determinar o número e localizações das infraestrutura a operar, as capacidades e o fluxo de mercadoria na rede logística.
This thesis describes a memetic algorithm applied to the design of a three-echelon logistics network over multiple periods with transportation mode selection and outsourcing. The memetic algorithm can be applied to an existing supply chain in order to obtain an optimized configuration or, if required, it can be used to define a new logistics network. In addition, production can be outsourced and direct shipments of products to customer zones are possible. In this problem, the capacity of an existing or new facility can be expanded over the time horizon. In this case, the facility cannot be closed. Existing facilities, once closed, cannot be reopened. New facilities cannot be closed, once opened. The heuristic is able to determine the number and locations of facilities (i.e. plants and warehouses), capacity levels as well as the flow of products throughout the supply chain.
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Filák, Jakub. "Evoluční optimalizace turnusů jízdních řádů." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2009. http://www.nusl.cz/ntk/nusl-236673.

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This thesis deals with the problem of vehicle scheduling in public transport. It contains a theoretical introduction to vehicles scheduling and evolutionary algorithms. Vehicle scheduling is analyzed with respect to the bus timetables. Analysis of evolutionary algorithms is done with emphasis on the genetic algorithms and tabu-search method After the theoretical introduction, a memetic algorithm for the given problem is analyzed. Finally, the thesis contains a description of the optimization system implementation and discusses the experiments with the system.
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Bonfim, Tatiane Regina. "Escalonamento memetico e neuro-memetico de tarefas." [s.n.], 2006. http://repositorio.unicamp.br/jspui/handle/REPOSIP/260503.

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Orientador: Akebo Yamakami
Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de Computação
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Resumo: Este trabalho apresenta uma nova abordagem de resolução, por algoritmo memético e pela coevolução de algoritmo memético com redes neurais, para o problema de escalonamento de tarefas em máquinas paralelas idênticas e para o problema de job shop com parâmetros precisos. Para os problemas de escalonamento com parâmetros com incertezas, onde os parâmetros não são precisamente conhecidos, toma-se dificil classificar um determinado escalonamento ótimo. A noção de ótimo também torna-se imprecisa e o grau de otimalidade de um dado escalonamento ("o quanto um escalonamento é ótimo") pode ser caracterizada por um número fuzzy. Foi aplicado também o conceito de otimalidade possível para medir a possibilidade de um determinado escalonamento ser ótimo. O algoritmo memético foi aplicado para encontrar soluções para o problema, a rede neural foi aplicada para encontrar a função de fitness das soluções encontradas pelo algoritmo memético, e o conceito de possibilidade foi aplicado para avaliar as melhores soluções. Foram utilizadas as redes neurais backpropagation e com aprendizado por reforço para encontrar o valor da função de fitness. As simulações mostraram que as redes neurais apresentaram uma boa performance na coevolução com o algoritmo memético e na resolução dos problemas, e mostraram que o conceito de possibilidade teve uma boa perfomance na avaliação da otimalidade das soluções
Abstract: This work presents a new approach for the resolution of the problem of identical parallel machine scheduling and job shop scheduling with precise parameters, with memetic algorithm and memetic algorithm coevolving with neural networks. For problems with parameters with uncertainties, where the parameters of the problem are not precisely known, it is difficult to say in prior which schedule will be optimal. The notion of optimal also becomes imprecise and the degree of optimality of a given schedule ("how much a schedule is optimal") can be characterized by a fuzzy number. We was used also the concepts of possibility to measure the possibility of a given schedule be optimal. Memetic algorithm has been used to find the solutions of the problem, the neural network has been used to find the fitness function of these solutions, and the concept of possibility has been used to evaluate the best solutions. We was used neural networks with backpropagation and reinforcement learning to find the fitness function. Simulations showed that the neural networks presents a good performance in the coevolution of the memetic algorithm and in the resolution of the problems, and showed that the concept of possibility present a good performance in the evaluation of solutions optimality
Doutorado
Telecomunicações e Telemática
Doutor em Engenharia Elétrica
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Procházka, Vít. "Pokročilé optimalizační modely v odpadovém hospodářství." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2014. http://www.nusl.cz/ntk/nusl-231395.

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This thesis deals with an optimization of waste collection in a mid-sized town. The model is formulated based on requirements from a real process. To deal with this problem, the original memetic algorithm was developed and implemented in C++.
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Buriol, Luciana Salete. "Algoritmo memetico para o problema do caixeiro viajante assimetrico como parte de um framework para algoritmos evolutivos." [s.n.], 2000. http://repositorio.unicamp.br/jspui/handle/REPOSIP/261832.

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Orientador: Paulo Morelato França
Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de Computação
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Resumo: Dentre a gama de técnicas heurísticas e exatas existentes para a resolução de problemas combinatórios, os algoritmos populacionais genéticos e meméticos têm se destacado devido a sua boa performance. Em especial, os algoritmos meméticos podem ser considerados atualmente como uma das técnicas melhores sucedidas para a resolução de vários problemas combinatórios, dentre eles, o problema do caixeiro viajante. Nesta dissertação será apresentado um algoritmo memético aplicado ao problema do caixeiro viajante assimétrico, com a proposta de uma nova busca local: Recursive Arc Insertion. Os resultados computacionais considerando as 27 instâncias assimétricas da TSPLIB são apresentados, analisados e comparados com resultados obtidos por outros métodos propostos para o problema. O mesmo algoritmo é também aplicado a 32 outras instâncias assimétricas e a 30 instâncias reduzidas do problema de ciclos hamiltonianos não direcionados. Um framework para algoritmos evolutivos é apresentado, já incluindo o algoritmo memético implementado e a redução de instâncias do problema de ciclos hamiltonianos não direcionados para o problema do caixeiro viajante simétrico. Além disso, dois geradores portáveis de instâncias com solução ótima conhecida são descritos: um para o problema do caixeiro viajante assimétrico e outro para o problema de ciclos hamiltonianos
Abstract: Among the range of heuristic and exact techniques for solving combinatorial problems, the genetic and memetic populational algorithms play an important role due to their good performance. In special, the memetic algorithms can be considered current1y as one of the best techniques to solve several combinatorial problems, especially, the traveling salesman problem. In this dissertation a memetic algorithm applied to the asymmetric traveling salesman problem is developed, and a new local search is proposed: Recursive Are Insertion. The computational results considering the 27 asymmetric instances from TSPLIB are presented, analyzed and compared with results attained by other methods recent1y published. The same algorithm is also applied to 32 other asymmetric instances and to 30 reduced instances from undirect hamiltonian cycle problem. A framework for evolutionary algorithms is also presented, including the memetic algorithm implemented and the codes which performs a reduction from the undirect hamiltonian cycle problem to the symmetric traveling salesman problem. Besides, two portable instances generators with a known optimal solution are described: one for asymmetric traveling salesman problem and other for hamiltonian cycle problem
Mestrado
Automação
Mestre em Engenharia Elétrica
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Dang, Hieu. "Adaptive multiobjective memetic optimization: algorithms and applications." Journal of Cognitive Informatics and Natural Intelligence, 2012. http://hdl.handle.net/1993/30856.

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The thesis presents research on multiobjective optimization based on memetic computing and its applications in engineering. We have introduced a framework for adaptive multiobjective memetic optimization algorithms (AMMOA) with an information theoretic criterion for guiding the selection, clustering, and local refinements. A robust stopping criterion for AMMOA has also been introduced to solve non-linear and large-scale optimization problems. The framework has been implemented for different benchmark test problems with remarkable results. This thesis also presents two applications of these algorithms. First, an optimal image data hiding technique has been formulated as a multiobjective optimization problem with conflicting objectives. In particular, trade-off factors in designing an optimal image data hiding are investigated to maximize the quality of watermarked images and the robustness of watermark. With the fixed size of a logo watermark, there is a conflict between these two objectives, thus a multiobjective optimization problem is introduced. We propose to use a hybrid between general regression neural networks (GRNN) and the adaptive multiobjective memetic optimization algorithm (AMMOA) to solve this challenging problem. This novel image data hiding approach has been implemented for many different test natural images with remarkable robustness and transparency of the embedded logo watermark. We also introduce a perceptual measure based on the relative Rényi information spectrum to evaluate the quality of watermarked images. The second application is the problem of joint spectrum sensing and power control optimization for a multichannel, multiple-user cognitive radio network. We investigated trade-off factors in designing efficient spectrum sensing techniques to maximize the throughput and minimize the interference. To maximize the throughput of secondary users and minimize the interference to primary users, we propose a joint determination of the sensing and transmission parameters of the secondary users, such as sensing times, decision threshold vectors, and power allocation vectors. There is a conflict between these two objectives, thus a multiobjective optimization problem is used again in the form of AMMOA. This algorithm learns to find optimal spectrum sensing times, decision threshold vectors, and power allocation vectors to maximize the averaged opportunistic throughput and minimize the averaged interference to the cognitive radio network.
February 2016
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Aldogan, Deniz. "Memetic Algorithms For Timetabling Problems In Private Schools." Master's thesis, METU, 2005. http://etd.lib.metu.edu.tr/upload/3/12606218/index.pdf.

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The aim of this study is to introduce a real-world timetabling problem that exists in some private schools in Turkey and to solve such problem instances utilizing memetic algorithms. Being a new type of problem and for privacy reasons, there is no real data available. Hence for benchmarking purposes, a random data generator has been implemented. Memetic algorithms (MAs) combining genetic algorithms and hill-climbing are applied to solve synthetic problem instances produced by this generator. Different types of recombination and mutation operators based on the hierarchical structure of the timetabling problem are proposed. A modified version of the violation directed hierarchical hill-climbing method (VDHC), introduced by A. Alkan and E. Ozcan, coordinates the process of 12 different low-level hill-climbing operators grouped in two distinct arrangements that attempt to resolve corresponding constraint violations. VDHC is an adaptive method advocating cooperation of hill-climbing operators. In addition, MAs with VDHC are compared with different versions of multimeme algorithms and pure genetic algorithms. Experimental results on synthetic benchmark data set indicate the success of the proposed MA.
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Caraffini, Fabio. "Novel memetic computing structures for continuous optimisation." Thesis, De Montfort University, 2014. http://hdl.handle.net/2086/10629.

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This thesis studies a class of optimisation algorithms, namely Memetic Computing Structures, and proposes a novel set of promising algorithms that move the first step towards an implementation for the automatic generation of optimisation algorithms for continuous domains. This thesis after a thorough review of local search algorithms and popular meta-heuristics, focuses on Memetic Computing in terms of algorithm structures and design philosophy. In particular, most of the design carried out during my doctoral studies is inspired by the lex parsimoniae, aka Ockham’s Razor. It has been shown how simple algorithms, when well implemented can outperform complex implementations. In order to achieve this aim, the design is always carried out by attempting to identify the role of each algorithmic component/operator. In this thesis, on the basis of this logic, a set of variants of a recently proposed algorithms are presented. Subsequently a novel memetic structure, namely Parallel Memetic Structure is proposed and tested against modern algorithms representing the state of the art in optimisation. Furthermore, an initial prototype of an automatic design platform is also included. This prototype performs an analysis on separability of the optimisation problem and, on the basis of the analysis results, designs some parts of the parallel structure. Promising results are included. Finally, an investigation of the correlation among the variables and problem dimensionality has been performed. An extremely interesting finding of this thesis work is that the degree of correlation among the variables decreases when the dimensionality increases. As a direct consequence of this fact, large scale problems are to some extent easier to handle than problems in low dimensionality since, due to the lack of correlation among the variables, they can effectively be tackled by an algorithm that performs moves along the axes.
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Fischer, Thomas. "Distributed memetic algorithms for graph theoretical combinatorial optimization problems." Berlin Logos-Verl, 2008. http://d-nb.info/994066945/04.

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Books on the topic "Algoritmi memetici"

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C, Tan K., Goh Chi-Keong, Ong Yew Soon, and SpringerLink (Online service), eds. Multi-Objective Memetic Algorithms. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009.

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Goh, Chi-Keong, Yew-Soon Ong, and Kay Chen Tan, eds. Multi-Objective Memetic Algorithms. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-88051-6.

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Neri, Ferrante, Carlos Cotta, and Pablo Moscato, eds. Handbook of Memetic Algorithms. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-23247-3.

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Carlos, Cotta, Moscato Pablo, and SpringerLink (Online service), eds. Handbook of Memetic Algorithms. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2012.

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Hart, William E., J. E. Smith, and N. Krasnogor, eds. Recent Advances in Memetic Algorithms. Berlin/Heidelberg: Springer-Verlag, 2005. http://dx.doi.org/10.1007/3-540-32363-5.

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Hemanth, D. Jude, B. Vinoth Kumar, and G. R. Karpagam Manavalan, eds. Recent Advances on Memetic Algorithms and its Applications in Image Processing. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-1362-6.

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Schönberger, Jörn. Operational freight carrier planning: Basic concepts, optimization models and advanced memetic algorithms. Berlin: Springer, 2005.

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Clancey, Owen. Dosimetric Optimization Method for CyberKnife Robotic RadioSurgery System Using a Memetic Algorithm. [New York, N.Y.?]: [publisher not identified], 2011.

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Swagatam, Das, Suganthan Ponnuthurai Nagaratnam, Nanda Pradipta Kumar, and SpringerLink (Online service), eds. Swarm, Evolutionary, and Memetic Computing: Third International Conference, SEMCCO 2012, Bhubaneswar, India, December 20-22, 2012. Proceedings. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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Nagaratnam, Suganthan Ponnuthurai, Das Swagatam, Satapathy Suresh Chandra, and SpringerLink (Online service), eds. Swarm, Evolutionary, and Memetic Computing: Second International Conference, SEMCCO 2011, Visakhapatnam, Andhra Pradesh, India, December 19-21, 2011, Proceedings, Part II. Berlin, Heidelberg: Springer-Verlag GmbH Berlin Heidelberg, 2011.

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Book chapters on the topic "Algoritmi memetici"

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Du, Ke-Lin, and M. N. S. Swamy. "Memetic Algorithms." In Search and Optimization by Metaheuristics, 315–25. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41192-7_19.

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Krasnogor, Natalio. "Memetic Algorithms." In Handbook of Natural Computing, 905–35. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-540-92910-9_29.

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Truszkowski, Walt, Christopher Rouff, Mohammad Akhavannik, and Edward Tunstel. "Memetic Algorithms." In SpringerBriefs in Electrical and Computer Engineering, 43–50. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-37952-0_4.

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Moscato, Pablo, Carlos Cotta, and Alexandre Mendes. "Memetic Algorithms." In New Optimization Techniques in Engineering, 53–85. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-39930-8_3.

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Cotta, Carlos, Luke Mathieson, and Pablo Moscato. "Memetic Algorithms." In Handbook of Heuristics, 607–38. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-07124-4_29.

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Cotta, Carlos, Luke Mathieson, and Pablo Moscato. "Memetic Algorithms." In Handbook of Heuristics, 1–32. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-07153-4_29-1.

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Jaszkiewicz, Andrzej, Hisao Ishibuchi, and Qingfu Zhang. "Multiobjective Memetic Algorithms." In Handbook of Memetic Algorithms, 201–17. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-23247-3_13.

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Gupta, Abhishek, and Yew-Soon Ong. "Canonical Memetic Algorithms." In Adaptation, Learning, and Optimization, 17–26. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-02729-2_2.

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Radcliffe, Nicholas J., and Patrick D. Surry. "Formal memetic algorithms." In Evolutionary Computing, 1–16. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/3-540-58483-8_1.

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Eiben, Ágoston E., and James E. Smith. "Evolutionary Algorithms." In Handbook of Memetic Algorithms, 9–27. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-23247-3_2.

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Conference papers on the topic "Algoritmi memetici"

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Misir, Mustafa, Stephanus Daniel Handoko, and Hoong Chuin Lau. "Building algorithm portfolios for memetic algorithms." In GECCO '14: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2014. http://dx.doi.org/10.1145/2598394.2598455.

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Costa, Celso M. da, Fernado L. Dotti, Eder N. Mathias, and Felipe M. Müller. "A Distributed Architecture Supporting Heuristic and Metaheuristic Optimization Methods." In Simpósio de Arquitetura de Computadores e Processamento de Alto Desempenho. Sociedade Brasileira de Computação, 2001. http://dx.doi.org/10.5753/sbac-pad.2001.22206.

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Abstract:
This paper presents a distributed software architecture that allows the cooperation among research institutions in the field of Operations Research. It has as main aims to share existing algorithms for optimization problems, to allow the easy testing of these algorithms with existing instances, and to share computational power among the cooperating institutions. This is achieved respecting the autonomy and heterogeneity of the cooperating institutions. The five functional blocks that build the architecture are discussed here and also a study case of a Parallel Memetic Algorithm to solve the Traveling Salesman Problem running on this environment is analysed.
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Porumbel, Daniel Cosmin, Jin-Kao Hao, and Pascale Kuntz. "Spacing memetic algorithms." In the 13th annual conference. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/2001576.2001720.

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Liu, Bo, Juan-Juan Xu, Bin Qian, Jian-Rong Wang, and Yan-Bin Chu. "Probabilistic memetic algorithm for flowshop scheduling." In 2013 IEEE Workshop on Memetic Computing (MC). IEEE, 2013. http://dx.doi.org/10.1109/mc.2013.6608208.

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Hongwei Mo and Longlong Meng. "Research on evolution hardware design based on memetic algorithm." In 2013 IEEE Workshop on Memetic Computing (MC). IEEE, 2013. http://dx.doi.org/10.1109/mc.2013.6608204.

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Wang, Juan, Ke Tang, and Xin Yao. "A memetic algorithm for uncertain Capacitated Arc Routing Problems." In 2013 IEEE Workshop on Memetic Computing (MC). IEEE, 2013. http://dx.doi.org/10.1109/mc.2013.6608210.

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Dinneen, Michael J., and Kuai Wei. "On the analysis of a (1+1) adaptive memetic algorithm." In 2013 IEEE Workshop on Memetic Computing (MC). IEEE, 2013. http://dx.doi.org/10.1109/mc.2013.6608203.

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Sun, Yiwen, Zexuan Zhu, Shan He, and Zhen Ji. "A coevolving memetic algorithm for simultaneous partitional clustering and feature weighting." In 2013 IEEE Workshop on Memetic Computing (MC). IEEE, 2013. http://dx.doi.org/10.1109/mc.2013.6608201.

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Jula, Amin, Zalinda Othman, and Elankovan Sundararajan. "A hybrid imperialist competitive-gravitational attraction search algorithm to optimize cloud service composition." In 2013 IEEE Workshop on Memetic Computing (MC). IEEE, 2013. http://dx.doi.org/10.1109/mc.2013.6608205.

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Yang, Cheng-San, Li-Yeh Chuang, Yu-Jung Chen, and Cheng-Hong Yang. "Feature Selection Using Memetic Algorithms." In 2008 Third International Conference on Convergence and Hybrid Information Technology (ICCIT). IEEE, 2008. http://dx.doi.org/10.1109/iccit.2008.81.

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