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Journal articles on the topic 'Memetic Evolution'

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Published: 9 March 2023

Last updated: 10 March 2023

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1

blackmore, susan. "implications for memetics." Behavioral and Brain Sciences 28, no. 4 (August 2005): 490. http://dx.doi.org/10.1017/s0140525x0523008x.

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the implications that steels & belpaeme's (s&b's) models have for memetics are discussed. the results demonstrate the power of memes (in this case colour words) to influence both concept formation, and the creation of innate concepts. they provide further evidence for the memetic drive hypothesis, with implications for the evolution of the human brain and for group differences in categorisation.

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2

Chen, Xinren. "Extensions of the Chinese passive construction." East Asian Pragmatics 2, no. 1 (April 28, 2017): 59–74. http://dx.doi.org/10.1558/eap.32412.

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The passive construction marked by bei + Vt in Chinese has undergone significant extensions in form, meaning, and function. Among a variety of existing accounts, the memetic discussions succeed in recognizing the new passive construction as a strong language meme and revealing some possible root causes for its transmission and replication, but leave much to be desired, such as how the form of the new construction contributes to its vigour, why the new construction is favoured by Chinese users over other means of expression with equivalent semantic meaning, and why new media can contribute to the transmission of the new construction. Thus, this study, while incorporating existing discussions, seeks to present a further treatment of the new phenomenon in the light of memetics. The author argues, on the basis of authentic data involving the use of the new passive construction, that (1) the new passive construction as a powerful memetic form of expression derives a major part of its replicating power from its parasitic nature, (2) the new passive construction as a memetic form of expression acquires its expressive power from its euphemistic effect appropriate to the Chinese sociocultural context, and (3) the new passive construction as a memetic form of expression owes its transmissive power to the easy and tolerant access and output enabled by new media. It is hoped that this study will reveal why and how the prototypical Chinese passive construction can undergo the aforementioned syntactic, semantic, and functional extensions characterisable as a process of memetic transmission in particular, and demonstrate how a memetic approach is applicable to the expatiation of syntactic evolution in general.

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3

Ermakov, Dmitry, and Alexander Ermakov. "Memetic approach to cultural evolution." Biosystems 204 (June 2021): 104378. http://dx.doi.org/10.1016/j.biosystems.2021.104378.

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4

Sutcliffe, A. G., and D. Wang. "Memetic reproduction and protolanguage evolution." Memetic Computing 4, no. 1 (February 8, 2012): 19–31. http://dx.doi.org/10.1007/s12293-012-0074-2.

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5

Mikhyeyev, A. N. "Ideal evolution and evolution of ideality." Faktori eksperimental'noi evolucii organizmiv 26 (September 1, 2020): 325–31. http://dx.doi.org/10.7124/feeo.v26.1289.

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On the basis of meta-scientific approaches, the legitimacy of considering the processes of biological evolution using the notions of attributiveness of the ideality of objects is substantiated. Ideal evolution is defined as the evolution of an object in the direction of the realization of its current and necessarily changing potentials, and the evolution of the ideality - as the evolution of forms, processes and functions. Thus, it is proposed that evolution must be considered as the realization of potentials and as a change in the ideal attribute of objects. Taking into account the ideal «component» allows to use almost all meta-scientific approaches to solve evolutionary problems – the theory of information, systems, fractals, risks etc. It is shown that memetics explores the general principles of information inheritance, regardless of its substrate, on which the genetic, epigenetic or memetic information itself. It is argued that in systemogenesis a certain potency (opportunity) is realized, generating a new potency, plan, idea or goal. Thus, it is impossible to describe all stages of evolution with one law. Each stage has its own substantial and organizational (actually «ideal», informational) component (attribute), the description of which requires appropriate laws. In Man, the role of the ideal or rather, informational, attribute is growing in the process of its progressive evolution, which turns into practically a «pure» informational evolution, isomorphic to its carrier. Keywords: meta-scientific approaches, ideal, information, progressive evolution, memetics, systemogenesis, emergence.

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6

Chesterman, Andrew. "The View from Memetics." PARADIGMI, no. 2 (July 2009): 75–88. http://dx.doi.org/10.3280/para2009-002007.

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- This essay proposes a memetic view of translation, as an alternative and perhaps more fruitful way of conceptualizing the issues involved. After a brief introduction to memetics as a theory of cultural transfer, we outline its relation to genetics and then consider its relevance for Translation Studies. Particular attention is given to a recent article by Maria Tymoczko which challenges some of the traditional assumptions of Translation Studies. Can memetics offer a way to meet these challenges? The essay closes with an assessment of some of the criticisms that have been directed against memetics.Keywords: Meme, Imitation, Cultural evolution, Transfer, Modification, Translation.Parole chiave: Meme, Imitazione, Evoluzione culturale, Trasmissione, Modificazione, Traduzione.

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Bartoccini, Umberto, Arturo Carpi, Valentina Poggioni, and Valentino Santucci. "Memes Evolution in a Memetic Variant of Particle Swarm Optimization." Mathematics 7, no. 5 (May 11, 2019): 423. http://dx.doi.org/10.3390/math7050423.

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In this work, a coevolving memetic particle swarm optimization (CoMPSO) algorithm is presented. CoMPSO introduces the memetic evolution of local search operators in particle swarm optimization (PSO) continuous/discrete hybrid search spaces. The proposed solution allows one to overcome the rigidity of uniform local search strategies when applied to PSO. The key contribution is that memes provides each particle of a PSO scheme with the ability to adapt its exploration dynamics to the local characteristics of the search space landscape. The objective is obtained by an original hybrid continuous/discrete meme representation and a probabilistic co-evolving PSO scheme for discrete, continuous, or hybrid spaces. The coevolving memetic PSO evolves both the solutions and their associated memes, i.e. the local search operators. The proposed CoMPSO approach has been experimented on a standard suite of numerical optimization benchmark problems. Preliminary experimental results show that CoMPSO is competitive with respect to standard PSO and other memetic PSO schemes in literature, and its a promising starting point for further research in adaptive PSO local search operators.

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8

Peng, Lei, Yanyun Zhang, Guangming Dai, and Maocai Wang. "Memetic Differential Evolution with an Improved Contraction Criterion." Computational Intelligence and Neuroscience 2017 (2017): 1–12. http://dx.doi.org/10.1155/2017/1395025.

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Memetic algorithms with an appropriate trade-off between the exploration and exploitation can obtain very good results in continuous optimization. In this paper, we present an improved memetic differential evolution algorithm for solving global optimization problems. The proposed approach, called memetic DE (MDE), hybridizes differential evolution (DE) with a local search (LS) operator and periodic reinitialization to balance the exploration and exploitation. A new contraction criterion, which is based on the improved maximum distance in objective space, is proposed to decide when the local search starts. The proposed algorithm is compared with six well-known evolutionary algorithms on twenty-one benchmark functions, and the experimental results are analyzed with two kinds of nonparametric statistical tests. Moreover, sensitivity analyses for parameters in MDE are also made. Experimental results have demonstrated the competitive performance of the proposed method with respect to the six compared algorithms.

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9

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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10

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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11

Maesani, Andrea, Giovanni Iacca, and Dario Floreano. "Memetic Viability Evolution for Constrained Optimization." IEEE Transactions on Evolutionary Computation 20, no. 1 (February 2016): 125–44. http://dx.doi.org/10.1109/tevc.2015.2428292.

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12

Mansueto, Pierluigi, and Fabio Schoen. "Memetic differential evolution methods for clustering problems." Pattern Recognition 114 (June 2021): 107849. http://dx.doi.org/10.1016/j.patcog.2021.107849.

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Tang, Deyu, Zhen Liu, Jie Zhao, Shoubin Dong, and Yongming Cai. "Memetic quantum evolution algorithm for global optimization." Neural Computing and Applications 32, no. 13 (August 19, 2019): 9299–329. http://dx.doi.org/10.1007/s00521-019-04439-8.

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14

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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Wang, Gao Ping, Meng Zhang, and Wei Wei Zhao. "A Novel Multiobjective Memetic Algorithm Based on IWO-DE and its Application in Nutrition Decision Making Problem." Advanced Materials Research 989-994 (July 2014): 1849–52. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.1849.

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In this paper ,we discuss multiobjective optimization problems solved by Memetic algorithms. We present A novel multiobjective memetic algorithm based on invasive weed optimization and differential evolution (IWO-DE) to solve this class of problems .We present the Nutrition Prescription Model for Meals.the IWO-DE is applied to solve the nutrition decision making problem to map the Pareto-optimum front. The results in the problem show its effectiveness.

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16

Chvaja, Radim. "Why Did Memetics Fail? Comparative Case Study." Perspectives on Science 28, no. 4 (August 2020): 542–70. http://dx.doi.org/10.1162/posc_a_00350.

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Although the theory of memetics appeared highly promising at the beginning, it is no longer considered a scientific theory among contemporary evolutionary scholars. This study aims to compare the genealogy of memetics with the historically more successful gene-culture coevolution theory. This comparison is made in order to determine the constraints that emerged during the internal development of the memetics theory that could bias memeticists to work on the ontology of meme units as opposed to hypotheses testing, which was adopted by the gene-culture scholars. I trace this problem back to the diachronic development of memetics to its origin in the gene-centered anti-group-selectionist argument of George C. Williams and Richard Dawkins. The strict adoption of this argument predisposed memeticists with the a priori idea that there is no evolution without discrete units of selection, which in turn, made them dependent on the principal separation of biological and memetic fitness. This separation thus prevented memeticists from accepting an adaptationist view of culture which, on the contrary, allowed gene-culture theorists to attract more scientists to test the hypotheses, creating the historical success of the gene-culture coevolution theory.

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17

Neri, Ferrante, and Ernesto Mininno. "Memetic Compact Differential Evolution for Cartesian Robot Control." IEEE Computational Intelligence Magazine 5, no. 2 (May 2010): 54–65. http://dx.doi.org/10.1109/mci.2010.936305.

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18

Langrish, John Z. "Darwinian Design: The Memetic Evolution of Design Ideas." Design Issues 20, no. 4 (September 2004): 4–19. http://dx.doi.org/10.1162/0747936042311968.

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19

Mininno, Ernesto, and Ferrante Neri. "A memetic Differential Evolution approach in noisy optimization." Memetic Computing 2, no. 2 (December 15, 2009): 111–35. http://dx.doi.org/10.1007/s12293-009-0029-4.

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20

Sutcliffe, A. G., and D. Wang. "Memetic evolution in the development of proto-language." Memetic Computing 6, no. 1 (December 18, 2013): 3–18. http://dx.doi.org/10.1007/s12293-013-0126-2.

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21

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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22

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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23

Carter, Brandon. "Hominid evolution: genetics versus memetics." International Journal of Astrobiology 11, no. 1 (September 9, 2011): 3–13. http://dx.doi.org/10.1017/s1473550411000279.

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AbstractThe last few million years on planet Earth have witnessed two remarkable phases of hominid development, starting with a phase of biological evolution characterized by rather rapid increase of the size of the brain. This has been followed by a phase of even more rapid technological evolution and concomitant expansion of the size of the population that began when our own particular ‘sapiens’ species emerged, just a few hundred thousand years ago. The present investigation exploits the analogy between the neo-Darwinian genetic evolution mechanism governing the first phase, and the memetic evolution mechanism governing the second phase. From the outset of the latter until very recently – about the year 2000 – the growth of the global population N was roughly governed by an equation of the form dN/Ndt=N/T*, in which T* is a coefficient introduced (in 1960) by von Foerster, who evaluated it empirically as about 200 000 million years. It is shown here how the value of this hitherto mysterious timescale governing the memetic phase is explicable in terms of what happened in the preceding genetic phase. The outcome is that the order of magnitude of the Foerster timescale can be accounted for as the product of the relevant (human) generation timescale, about 20 years, with the number of bits of information in the genome, of the order of 10 000 million. Whereas the origin of our ‘homo’ genus may well have involved an evolutionary hard step, it transpires that the emergence of our particular ‘sapiens’ species was rather an automatic process.

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24

Drapal, Andrej. "Innovation from a Memetic and Evolutionary Perspective." Mednarodno inovativno poslovanje = Journal of Innovative Business and Management 11, no. 1 (May 15, 2019): 11–15. http://dx.doi.org/10.32015/jimb/2019-11-1-2.

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Innovation happens all around the Universe and is a fundamental mechanism of evolution. Living creatures cannot but exist and develop through innovation. Innovation happens through mutations that make sense in retrospect only. Natural selection makes certain mutations as beneficial and other as something that was already forgotten. As much as genes serve as quantized smaller units where innovation takes place in physical bodies, memes serve as basic quantized units of human culture. Memes as second replicators to genes obey similar laws of evolution and thus innovation as genes. Innovation can be propagated only by removing obstacles on one side and by allowing mutations to emerge as long as they do not destroy identity.

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25

Akıner, İlknur, İbrahim Yitmen, Muhammed Ernur Akıner, and Nurdan Akıner. "The Memetic Evolution of Latin American Architectural Design Culture." Buildings 11, no. 7 (July 3, 2021): 288. http://dx.doi.org/10.3390/buildings11070288.

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Architecture is an evolutionary field. Through time, it changes and adapts itself according to two things: the environment and the user, which are the touchstones of the concept of culture. Culture changes in long time intervals because of its cumulative structure, so its effects can be observed on a large scale. A nation displays itself with its culture and uses architecture as a tool to convey its cultural identity. This dual relationship between architecture and culture can be observed at various times and in various lands, most notably in Latin American designers. The geographical positions of Latin American nations and their political situations in the twentieth century leads to the occurrence of a recognizable cultural identity, and it influenced the architectural design language of that region. The nonlinear forms in architecture were once experienced commonly around Latin America, and this design expression shows itself in the designers’ other works through time and around the world. The cultural background of Latin American architecture investigated within this study, in terms of their design approach based upon the form and effect of Latin American culture on this architectural design language, is examined with the explanation of the concept of culture by two leading scholars: Geert Hofstede and Richard Dawkins. This paper nevertheless puts together architecture and semiology by considering key twentieth century philosophers and cultural theorist methodologies. Cultural theorist and analyst Roland Barthes was the first person to ask architects to examine the possibility of bringing semiology and architectural theory together. Following an overview of existing semiological conditions, this paper analyzed Roland Barthes and Umberto Eco’s hypothesis of the semiological language of architectural designs of Latin American designers by examining their cultural origin. The work’s findings express the historical conditions that enabled the contemporary architecture and culture study of Latin America between 1945 and 1975 to address the “Latin American model” of architectural modernism.

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Al-Jadir, Ibraheem, Kok Wai Wong, Chun Che Fung, and Hong Xie. "Unsupervised Text Feature Selection Using Memetic Dichotomous Differential Evolution." Algorithms 13, no. 6 (May 26, 2020): 131. http://dx.doi.org/10.3390/a13060131.

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Feature Selection (FS) methods have been studied extensively in the literature, and there are a crucial component in machine learning techniques. However, unsupervised text feature selection has not been well studied in document clustering problems. Feature selection could be modelled as an optimization problem due to the large number of possible solutions that might be valid. In this paper, a memetic method that combines Differential Evolution (DE) with Simulated Annealing (SA) for unsupervised FS was proposed. Due to the use of only two values indicating the existence or absence of the feature, a binary version of differential evolution is used. A dichotomous DE was used for the purpose of the binary version, and the proposed method is named Dichotomous Differential Evolution Simulated Annealing (DDESA). This method uses dichotomous mutation instead of using the standard mutation DE to be more effective for binary purposes. The Mean Absolute Distance (MAD) filter was used as the feature subset internal evaluation measure in this paper. The proposed method was compared with other state-of-the-art methods including the standard DE combined with SA, which is named DESA in this paper, using five benchmark datasets. The F-micro, F-macro (F-scores) and Average Distance of Document to Cluster (ADDC) measures were utilized as the evaluation measures. The Reduction Rate (RR) was also used as an evaluation measure. Test results showed that the proposed DDESA outperformed the other tested methods in performing the unsupervised text feature selection.

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27

Miller, Geoffrey F. "Memetic Evolution and Human CultureThe Meme Machine. Susan Blackmore." Quarterly Review of Biology 75, no. 4 (December 2000): 434–36. http://dx.doi.org/10.1086/393623.

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Caponio, Andrea, Ferrante Neri, and Ville Tirronen. "Super-fit control adaptation in memetic differential evolution frameworks." Soft Computing 13, no. 8-9 (August 2, 2008): 811–31. http://dx.doi.org/10.1007/s00500-008-0357-1.

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Kaur, Manjit, Vijay Kumar, and Li Li. "Color image encryption approach based on memetic differential evolution." Neural Computing and Applications 31, no. 11 (July 19, 2018): 7975–87. http://dx.doi.org/10.1007/s00521-018-3642-7.

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Chłopek, Patryk. "The Forgotten meme. On the example of Life and works of Placyd Jankowski." Studia Slavica XXV, no. 1 (October 2021): 29–41. http://dx.doi.org/10.15452/studiaslavica.2021.25.0003.

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Placyd Jankowski (known also as John of Dycalp) was a popular writer active in the 1840s, he quickly gained popularity thanks to his humorous lyrics, but before his death in 1872 he was almost forgotten by readers. This raises questions about the rules guiding cultural memory. One of the concepts that could explain the process of cultural heredity is the memetic theory proposed by Richard Dawkins. The scholar assumes the existence of cultural units subject to the process of evolution, which he called memes. Their success and survival depend on the fulfillment of specific characteristics to which the human mind is particularly susceptible, i.e. topicality, originality, narrative, and universality. Using the tools developed by memetics, the article will attempt to describe the reasons for social forgetting on the example of the life and work of Placyd Jankowski.

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Karpenko, A. P., and M. K. Sakharov. "New Adaptive Multi-Memetic Global Optimization Agorithm." Herald of the Bauman Moscow State Technical University. Series Natural Sciences, no. 83 (2019): 17–31. http://dx.doi.org/10.18698/1812-3368-2019-2-17-31.

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This paper deals with the Simple MEC (SMEC) algorithm which belongs to a class of MEC algorithms. The algorithm was selected for investigation due to the following reasons: nowadays this algorithm and its modifications are successfully used for solving various optimization problems; the algorithm is highly suitable for parallel computations, especially for loosely coupled systems; the algorithm is not sufficiently studied --- there are relatively few modifications of SMEC (while, for instance, tens of various modifications are known for particle swarm optimization). Authors proposed an adaptive multi-memetic modification of SMEC algorithm, which includes a stage of landscape analysis for composing a set of basic adaptation strategies; software implementation of the algorithm is also presented. Performance investigation was carried out with a use of multi-dimensional benchmark functions of different classes. It was demonstrated that the concept of multi-population along with the incorporated landscape analysis procedure allows making a rough static adaptation of the algorithm to the objective function at the very beginning of evolution process at the cost of small computational expenses. Utilization of memes, in turn, helps the algorithm to correct possible errors of static adaptation during the evolution due to a closer investigation of search sub-domains

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Sabar, Nasser R., Jemal Abawajy, and John Yearwood. "Heterogeneous Cooperative Co-Evolution Memetic Differential Evolution Algorithm for Big Data Optimization Problems." IEEE Transactions on Evolutionary Computation 21, no. 2 (April 2017): 315–27. http://dx.doi.org/10.1109/tevc.2016.2602860.

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Mandal, Ankush, Hamim Zafar, Swagatam Das, and Athanassios V. Vasilakos. "EFFICIENT CIRCULAR ARRAY SYNTHESIS WITH A MEMETIC DIFFERENTIAL EVOLUTION ALGORITHM." Progress In Electromagnetics Research B 38 (2012): 367–85. http://dx.doi.org/10.2528/pierb11111802.

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Lin, Chien-Chou, Kun-Cheng Chen, and Wei-Ju Chuang. "Motion Planning Using a Memetic Evolution Algorithm for Swarm Robots." International Journal of Advanced Robotic Systems 9, no. 1 (January 1, 2012): 19. http://dx.doi.org/10.5772/45669.

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A hierarchical memetic algorithm (MA) is proposed for the path planning and formation control of swarm robots. The proposed algorithm consists of a global path planner (GPP) and a local motion planner (LMP). The GPP plans a trajectory within the Voronoi diagram (VD) of the free space. An MA with a non-random initial population plans a series of configurations along the path given by the former stage. The MA locally adjusts the robot positions to search for better fitness along the gradient direction of the distance between the swarm robots and the intermediate goals (IGs). Once the optimal configuration is obtained, the best chromosomes are reserved as the initial population for the next generation. Since the proposed MA has a non-random initial population and local searching, it is more efficient and the planned path is faster compared to a traditional genetic algorithm (GA). The simulation results show that the proposed algorithm works well in terms of path smoothness and computation efficiency.

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35

Jan, Steven. "Memetic Perspectives on the Evolution of Tonal Systems in Music." Interdisciplinary Science Reviews 40, no. 2 (June 2015): 145–67. http://dx.doi.org/10.1179/0308018815z.000000000110.

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Mustafa, Hossam M. J., Masri Ayob, Dheeb Albashish, and Sawsan Abu-Taleb. "Solving text clustering problem using a memetic differential evolution algorithm." PLOS ONE 15, no. 6 (June 11, 2020): e0232816. http://dx.doi.org/10.1371/journal.pone.0232816.

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Shen, Xiaoning, and Min Zhang. "A differential evolution-based memetic algorithm for project scheduling problems." International Journal of Innovative Computing and Applications 6, no. 3/4 (2015): 229. http://dx.doi.org/10.1504/ijica.2015.072987.

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Li, Shuijia, Wenyin Gong, Xuesong Yan, Chengyu Hu, Danyu Bai, and Ling Wang. "Parameter estimation of photovoltaic models with memetic adaptive differential evolution." Solar Energy 190 (September 2019): 465–74. http://dx.doi.org/10.1016/j.solener.2019.08.022.

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Subudhi, Bidyadhar, and Debashisha Jena. "Nonlinear system identification using memetic differential evolution trained neural networks." Neurocomputing 74, no. 10 (May 2011): 1696–709. http://dx.doi.org/10.1016/j.neucom.2011.02.006.

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Dhaliwal, Jatinder Singh, and J. S. Dhillon. "Profit based unit commitment using memetic binary differential evolution algorithm." Applied Soft Computing 81 (August 2019): 105502. http://dx.doi.org/10.1016/j.asoc.2019.105502.

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Lin, Yung Chien. "A Memetic Algorithm for Mixed-Integer Optimization Problems." Applied Mechanics and Materials 284-287 (January 2013): 2970–74. http://dx.doi.org/10.4028/www.scientific.net/amm.284-287.2970.

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Evolutionary algorithms (EAs) are population-based global search methods. Memetic Algorithms (MAs) are hybrid EAs that combine genetic operators with local search methods. With global exploration and local exploitation in search space, MAs are capable of obtaining more high-quality 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 is developed for solving mixed-integer constrained optimization problems. The proposed algorithm is implemented and tested on a benchmark mixed-integer constrained optimization problem. Experimental results show that the proposed algorithm can find a better optimal solution compared with some other search algorithms.

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Lin, Yung Chien. "A Mixed-Integer Memetic Algorithm Applied to Batch Process Optimization." Applied Mechanics and Materials 300-301 (February 2013): 645–48. http://dx.doi.org/10.4028/www.scientific.net/amm.300-301.645.

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Abstract:

Evolutionary algorithms (EAs) are population-based global search methods. Memetic Algorithms (MAs) are hybrid EAs that combine genetic operators with local search methods. With global exploration and local exploitation in search space, MAs are capable of obtaining more high-quality 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 mixed-integer memetic algorithm based on MIHDE is developed for solving mixed-integer constrained optimization problems. The proposed algorithm is implemented and applied to the optimal design of batch processes. Experimental results show that the proposed algorithm can find a better optimal solution compared with some other search algorithms.

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Dong, Ning, and Yuping Wang. "A Memetic Differential Evolution Algorithm Based on Dynamic Preference for Constrained Optimization Problems." Journal of Applied Mathematics 2014 (2014): 1–15. http://dx.doi.org/10.1155/2014/606019.

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Abstract:

The constrained optimization problem (COP) is converted into a biobjective optimization problem first, and then a new memetic differential evolution algorithm with dynamic preference is proposed for solving the converted problem. In the memetic algorithm, the global search, which uses differential evolution (DE) as the search scheme, is guided by a novel fitness function based on achievement scalarizing function (ASF). The novel fitness function constructed by a reference point and a weighting vector adjusts preference dynamically towards different objectives during evolution, in which the reference point and weighting vector are determined adapting to the current population. In the local search procedure, simplex crossover (SPX) is used as the search engine, which concentrates on the neighborhood embraced by both the best feasible and infeasible individuals and guides the search approaching the optimal solution from both sides of the boundary of the feasible region. As a result, the search can efficiently explore and exploit the search space. Numerical experiments on 22 well-known benchmark functions are executed, and comparisons with five state-of-the-art algorithms are made. The results illustrate that the proposed algorithm is competitive with and in some cases superior to the compared ones in terms of the quality, efficiency, and the robustness of the obtained results.

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Khitruk, Ekaterina. "Thinking of a human in the context of the memetic theory of religion: from the “image and likeness” to the “vehicle”." St.Tikhons' University Review 101 (June 30, 2022): 118–35. http://dx.doi.org/10.15382/sturi2022101.118-135.

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The article is devoted to the study of the idea of a human in the context of the modern memetic theory of religion. The work consistently reveals the main provisions of the memetic concept in the works of Richard Dawkins, Daniel Dennett and Susan Blackmore. Richard Dawkins is regarded as the founder of the replicator-centric interpretation of the evolutionary process, which contributed to the formation of the idea of the meme as a unit of cultural information. Memes compete with each other in the process of cultural evolution, pursuing their own benefits, regardless of the possible benefits of host organisms. To such "selfish" memes, R. Dawkins, first of all, refers to religious ideas. R. Dawkins' concept has a popular scientific character and its author, considering a person to be a bearer of memes, does not clarify the ways of interaction between specific memes and the human mind. This philosophical aspect of the memetic concept is developed in the theory of Daniel Dennett, who, distracting from the scientific (biological) context, builds a naturalistic metanarrative based on the concept of memes. D. Dennett argues that the human mind is not only an effective means of transportation for memes, but also literally an artifact created in the process of memetic evolution and acquired, thanks to it, a tendency to select and prefer certain (evolutionarily successful) types of memes ... Such an interpretation of man is consolidated in the psychological theory of S. Blackmore, who asserts that the theory of memes finally and convincingly debunks both the religious ideas about the free and autonomous personality of God, who is responsible for the existence of the world, and the traditional ideas about the free and autonomous personality of man, who is responsible both for his own existence in general, and, in particular, for the morally significant decisions of their lives. S. Blackmore proposes to consolidate the new concept of a person as a vehicle with appropriate psychological techniques that get rid of the "false" feelings of their own autonomy and freedom. The author of this article considers the memetic concept of religion, culture and man as a non-trivial version of classical naturalism and suggests the possible productivity of criticism of this concept based on the unique Christian interpretation of personality as irreducibility to nature.

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Mishra, K. K., Ashish Tripathi, Shailesh Tiwari, and Nitin Saxena. "Evolution based memetic algorithm and its application in software cost estimation." Journal of Intelligent & Fuzzy Systems 32, no. 3 (February 24, 2017): 2485–98. http://dx.doi.org/10.3233/jifs-16463.

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Wang, Yuan, Zhenguo Ding, Mingchen Zuo, and Lei Peng. "An improved memetic differential evolution for college students' comprehensive quality evaluation." International Journal of Wireless and Mobile Computing 13, no. 3 (2017): 193. http://dx.doi.org/10.1504/ijwmc.2017.088528.

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Peng, Lei, Mingchen Zuo, Zhenguo Ding, and Yuan Wang. "An improved memetic differential evolution for college students' comprehensive quality evaluation." International Journal of Wireless and Mobile Computing 13, no. 3 (2017): 193. http://dx.doi.org/10.1504/ijwmc.2017.10009496.

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Wu, Xueqi, and Ada Che. "A memetic differential evolution algorithm for energy-efficient parallel machine scheduling." Omega 82 (January 2019): 155–65. http://dx.doi.org/10.1016/j.omega.2018.01.001.

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Liu, Bo, Jing Lu, Yan Wang, and Yang Tang. "An effective parameter extraction method based on memetic differential evolution algorithm." Microelectronics Journal 39, no. 12 (December 2008): 1761–69. http://dx.doi.org/10.1016/j.mejo.2008.02.021.

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Wang, Xi, Mengmeng Sheng, Kangfei Ye, Jian Lin, Jiafa Mao, Shengyong Chen, and Weiguo Sheng. "A multilevel sampling strategy based memetic differential evolution for multimodal optimization." Neurocomputing 334 (March 2019): 79–88. http://dx.doi.org/10.1016/j.neucom.2019.01.006.

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