Memetic Research Articles

Real-time inverse kinematics for robotic manipulation under remote center-of-motion constraint using memetic evolution

Real-time inverse kinematics for robotic manipulation under remote center-of-motion constraint using memetic evolution

Improved memetic programming algorithm

Improved memetic programming algorithm

A memetic evolution system with statistical variable classification for large-scale many-objective optimization

Although the optimization algorithms have been widely studied, the large-scale many-objective optimization problems (LSMaOPs) remain challenging. Due to the existence of a large number of decision variables, it is necessary to carry out decision variable analysis. However, it is often difficult to discriminate the diversity-related and convergence-related variables when the problem has complex characteristics. Meanwhile, as the number of decision variables and the number of objectives increase, many algorithms will suffer from the convergence challenge. To overcome these challenges, this paper proposes a Memetic Evolution System with Statistical Variable Classification (MES-SVC). A statistical variable classification method is proposed to discriminate the convergence-related and the diversity-related variables. A memetic evolution system, which includes a memetic exploitation and exploration module, and a memetic elite imitation module, is proposed to make information guidance during the evolution, thereby promote convergence. The performance of MES-SVC is compared with the state-of-the-art algorithms on 50 test instances with 3 to 10 objectives and 300 to 1000 decision variables. Experimental studies demonstrate the promising performance of the proposed MES-SVC in terms of both diversity and convergence of solutions.

The Memetic Evolution of Latin American Architectural Design Culture

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.

Improved Memetic Programming algorithm

Improved Memetic Programming algorithm

Delayed dynamic step shuffling frog-leaping algorithm for optimal design of photovoltaic models

Developing an accurate mathematical model is an essential tool for studying and optimizing the performance of the photovoltaic cell system (PV). Transforming the PV problem into an optimization problem in which meta-heuristic algorithms excel provides an alternative approach to identifying the PV model’s parameters. The memetic evolution mechanism and the shuffling strategy included in the shuffling frog-leaping algorithm (SFLA) provide safeguards for solving nonlinear, multimodal, and high-dimensional problems. However, the low convergence accuracy is an essential drawback of the SFLA algorithm to solve the PV problem. This paper proposes a delayed dynamic step mechanism based on the SFLA algorithm’s characteristics to overcome this disadvantage, called the DDSFLA algorithm. The results of testing 23 benchmark functions and extracting the single diode model, the double-diode model, and the PV module show that the DDSFLA algorithm has a faster convergence speed and higher convergence accuracy and exhibits strong optimization stability under the special conditions of different temperatures or light intensities. The results suggest that the proposed algorithm can be used as an effective method to handle PV models’ parameter extraction. The extra resources and online user guidance for this research will be provided at https://aliasgharheidari.com.

Crowding out Memetic Explanation

Memes have been proposed to explain wide swathes of human culture and language use. I argue that what is really doing the explanatory work in many of these cases is a basic mechanism of information transmission, which is distinct from memetic evolution by natural selection in significant ways. Perhaps the most significant of these is that information transmission depends primarily on the interests of the users of information, rather than the reproductive interests of the informational entities—‘memes’—themselves. Although my main target is memetic approaches, this argument also applies to some other, nonmemetic, theories of cultural evolution.

Memetic Relationships as Tillet’s Shared Characteristics

The field of knowledge organization, and cataloguing in particular, has increasingly become concerned with bibliographic relationships. Tillett (2001) developed a taxonomy of bibliographic relationships that is largely shared by Functional Requirements for Bibliographic Records (FRBR), with the exception of the “shared characteristic” relationship including such features as shared creator or subject headings. This paper will offer another possible shared characteristic: “memes.” Memes are units of cultural inheritance and include literary tropes, character archetypes, and genre conceits, and can link otherwise unconnected works. Le domaine de l'organisation des connaissances, et du catalogage en particulier, se préoccupe de plus en plus des relations bibliographiques. Tillett (2001) a développé une taxonomie des relations bibliographiques qui est largement partagée par les Functional Requirements for Bibliographic Records (FRBR), à l'exception de la relation «caractéristique partagée» incluant des caractéristiques telles que le partage de créateur ou de vedettes-matière. Cet article proposera une autre caractéristique commune possible: les mèmes. Les mèmes sont des unités d'héritage culturel qui comprennent des tropes et des archétypes de personnages et de genre, et qui peuvent lier des œuvres autrement non liées.

The Genealogy of Bram Stoker's Dracula

British author of Irish origin, Bram Stoker’s gothic horror novel, Dracula is the most reputed and popular example of the vampire literature that first emerged in seventeenth-century poetry. The first of the two key concepts that this thesis analyzes is the concept of “meme”, which was coined by the British evolutionary biologist Richard Dawkins, that can be defined as a thought, symbol or application transmitted from one individual to another via oral, written and visual methods and means of communication within a culture, replicates itself, transforms, responds to selective pressures, and the second concept is the memetic evolution of the vampire into the character, Dracula by Bram Stoker in Romantic ballads and Victorian horror narratives within the context of Evolutionary Literary Criticism which was theorized by Professor Joseph Carroll. In this regard, the qualities of the vampire phenotype that the Dracula meme inherited some qualities from its antecedent works of vampire fiction through memetic heredity, setting and plot are examined, and the evolutionary process that literary vampire predecessor had undergone towards the Transylvanian, aristocratic, seductive, stereotypical vampire. Finally, in the appendix, the prequels, sequels, and spin-offs of Dracula to demonstrate that the Dracula meme has survived and thrived notwithstanding over a century after its first publication and the selective pressures, and subsist by copying itself.

Memes, memeiosis, and memetic drift: Cheryl’s Chichier She Shed

Memes, memeiosis, and memetic drift: Cheryl’s Chichier She Shed

MILP Modeling and Optimization of Energy- Efficient Distributed Flexible Job Shop Scheduling Problem

With the global warming problem and increasing energy cost, manufacturing firms are paying more and more attention to reducing energy consumption. This paper addresses the distributed flexible job shop scheduling problem (DFJSP) with minimizing energy consumption. To solve the problem, firstly, a novel mixed integer linear programming (MILP) model is developed to solve small-scaled problems to optimality. Due to the NP-hardness of DFJSP, we then propose an efficient hybrid shuffled frog-leaping algorithm (HSFLA) for solving DFJSP, particularly for large-sized problems. HSFLA combines the shuffled frog-leaping algorithm (SFLA) with powerful global search ability and variable neighborhood search (VNS) with good local search ability. Moreover, in HSFLA, the encoding method, the decoding method, the initialization method and the memetic evolution process are specifically designed. Finally, numerical experiments are conducted to evaluate the performance of the proposed MILP model and HFSLA.

New framework for optimizing best management practices at multiple scales

The optimal design of best management practices (BMPs) is of vital importance for the control of nonpoint source (NPS) pollutants, but proper methods for multiple-scale BMP design are still lacking, especially for large-scale watersheds. In this study, a new framework (MS-BMPs) was proposed for multiscale BMP design by combining multiscale design ideas, hydrological models, cost estimators and shuffled frog leaping algorithms. In addition, the computation efficiency of BMP design was improved by modifying the memetic evolution process. This new framework was then tested in the Daning River Watershed, Three Gorges Reservoir Region, China. Based on the results, the new framework could provide more powerful convergence and optimization ability, while the rationality of BMP configuration was increased by four times. From the Pareto front of the MS-BMPs, the maximum removal of TN and TP improved by 19.42% and 14.36%, indicating a more cost-effective BMP design. This study also highlighted the priority of fertilization management in the regulation of NPS pollution. This new framework can be easily extended to any watershed to assist managers with the optimal design of BMPs.

A Multi-strategy Shuffled Frog-leaping Algorithm for Numerical Optimization

This study proposes a multi-strategy shuffled frog-leaping algorithm for numerical optimization (MSSFLA), which combines the merits of a frog-leaping step rule, the crossover operator, and a novel recursive programming. First, the frog-leaping step rule depends on the level of attractive effect between the worst frog and other frogs in a memeplex, which utilizes the advantages of frogs around the worst frog, making the worst frog more conducive to the evolution direction of the whole population. Second, the crossover operator of the genetic algorithm is used for yielding new frogs based on the best and worst individual frog instead of the random mechanism in the original shuffled frog-leaping algorithm (SFLA). The crossover operation aims to enhance population diversity and conduciveness to the memetic evolution of each memeplex. Finally, recursive programming is presented to store the results of preceding attempts as basis for the computation of those that succeed, which will help save a large number of repeated computing resources in a local search. Experiment results show that MSSFLA has better performance than other algorithms on the convergence and searching effectivity. Therefore, it can be considered as a more competitive improved algorithm for SFLA on the efficiency and accuracy of the best solution.

SOLVING TWO-CLASS CLASSIFICATION PROBLEM USING MEMETIC PROGRAMMING

SOLVING TWO-CLASS CLASSIFICATION PROBLEM USING MEMETIC PROGRAMMING

Memetic Evolution for Generic Full-Body Inverse Kinematics in Robotics and Animation

In this paper, a novel and fast memetic evolutionary algorithm is presented which can solve fully constrained generic inverse kinematics with multiple end effectors and goal objectives, leaving high flexibility for the design of custom cost functions. The algorithm utilizes a hybridization of evolutionary and swarm optimization, combined with the limited-memory-Broyden-Fletcher-Goldfarb-Shanno with bound constraints algorithm for gradient-based optimization. Accurate solutions can be found in real-time and suboptimal extrema are robustly avoided, scaling well even for greatly higher degree of freedom. The algorithm provides a general framework for bounded continuous optimization which only requires two parameters for the number of individuals and elites to be set, and supports adding additional goals and constraints for inverse kinematics, such as minimal displacement between solutions, collision avoidance, or functional joint relations. Experimental results on several industrial and anthropomorphic robots as well as on virtual characters demonstrate the algorithm to be applicable for solving complex kinematic postures for different challenging tasks in robotics, human-robot interaction and character animation, including dexterous object manipulation, collision-free full-body motion, as well as animation post-processing for video games and films. Implementations are made available for Unity3D and robot operating system.

Memes Evolution in a Memetic Variant of Particle Swarm Optimization

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.

Bounded, Multidimensional, Integrated Memetic Evolution for Character Recognition Based on Predictive Elimination Theory and Optimization Techniques

This article describes how inspired by the natural process of evolution in genetic algorithms, memetic algorithms (MAs) are a category of cultural evolution phenomenon. The very concept of MA has been discussed in the last few years and is adding newer dimensions to MA and computational skills of algorithms. There are many optimization algorithms which fully exploit the problem under consideration. This article presents a heuristic approach for an improvised algorithm which takes into consideration various optimization parameters in isolation and tries to integrate the self-learning technique of MA. A general structure of MA according to this article should be perfectly in-line with brain activities which are neurotically tested and given maximum emphasis on local search and context-based predictive approaches rather than mathematically computing every event and taking or picking solutions based on results of certain formula. This article goes one step beyond the conventional set of the variety of problem domains, ranging from discrete optimization, continuous optimization, constrained optimization and multi objective optimization in which MAs have been successfully implemented. These optimization techniques must be processed using outcomes of predictive optimization and using a method of elimination to make the search set smaller and smaller as we progress deeper into the search. There is a scarcity of literature and also lack of availability of comprehensive reviews on MAs. The proposed technique is a better approach for solving combinatorial optimization problems. This article gives an overview of various domains and problem types in which MA can be used. Apart from this, the problem of character recognition using predictive optimization and implementation of elimination theory MA is discussed.

박경리 『토지』변용작품들의 원작 전유 방식 연구

박경리 『토지』변용작품들의 원작 전유 방식 연구

"The Two Brothers": Reconciling Perceptual-Cognitive and Statistical Models of Musical Evolution.

While the “units, events and dynamics” of memetic evolution have been abstractly theorized (Lynch, 1998), they have not been applied systematically to real corpora in music. Some researchers, convinced of the validity of cultural evolution in more than the metaphorical sense adopted by much musicology, but perhaps skeptical of some or all of the claims of memetics, have attempted statistically based corpus-analysis techniques of music drawn from molecular biology, and these have offered strong evidence in favor of system-level change over time (Savage, 2017). This article argues that such statistical approaches, while illuminating, ignore the psychological realities of music-information grouping, the transmission of such groups with varying degrees of fidelity, their selection according to relative perceptual-cognitive salience, and the power of this Darwinian process to drive the systemic changes (such as the development over time of systems of tonal organization in music) that statistical methodologies measure. It asserts that a synthesis between such statistical approaches to the study of music-cultural change and the theory of memetics as applied to music (Jan, 2007), in particular the latter's perceptual-cognitive elements, would harness the strengths of each approach and deepen understanding of cultural evolution in music.

A new hybrid memetic multi-objective optimization algorithm for multi-objective optimization

To deal with the multi-objective optimization problems (MOPs), a meta-heuristic based on an improved shuffled frog leaping algorithm (ISFLA) which belongs to memetic evolution is presented. For the MOPs, both diversity maintenance and searching effectiveness are crucial for algorithm evolution. In this work, modified calculation of crowding distance to evaluate the density of a solution, memeplex clustering analyses based on a grid to divide the population, and new selection measure of global best individual are proposed to ensure the diversity of the algorithm. A multi-objective extremal optimization procedure (MEOP) is also introduced and incorporated into ISFLA to enable the algorithm to evolve more effectively. Finally, the experimental tests on thirteen unconstrained MOPs and DTLZ many-objective problems show that the proposed algorithm is flexible to handle MOPs and many-objective problems. The effectiveness and robustness of the proposed algorithm are also analyzed in detail.