Memetic Approach Research Articles

Quality of Accommodation Services – The Memetic Approach

The main scientific objective of the article is to demonstrate, on the example of accommodation service, the possibility of applying the netnographic methodology and meme theory in the context of the analysis of the manner of perception of the quality of a service. The cause-and-effect relationship between the service quality and the formation of memetic transfer was defined, which, through the creation of specific consumer expectations, can contribute to the creation of a positive image of the service provider. The obtained results will provide a theoretical basis for an innovative model for the management of the quality of service.

Memetic approach for irremediable ill-conditioned parametric inverse problems * *The work presented in this paper has been partially supported by Polish National Science Center grants no. DEC-2015/17/B/ST6/01867 and by the AGH statutory research grant no. 11.11.230.124.

Abstract The paper introduces a new taxonomy of ill-posed parametric inverse problems, formulated as global optimization ones. It systematizes irremediable problems, which appear quite often in the real life but cannot be solved using the regularization method. The paper also shows a new way of solving irremediable inverse problems by a complex memetic approach including: genetic computation with adaptive accuracy, random sample clustering and a sophisticated local approximation of misfit plateau regions. Finally, we use a benchmark function featuring cross-shaped plateau to discuss some factors that influence the quality of plateau shape approximation.

Intelligence Classification of the Timetable Problem: A Memetic Approach

Scheduling tasks persist in our daily functioning and as academia, abounds more in University circle as hard-NP constraint satisfaction tasks. Many studies exist with the objective of resolving the many conflicted constraints that exists in a timetable schedule using various algorithms. Many of such algorithms simply search the domain space for a goal state that satisfies the problem constraints. Our study yields an outcome assignment that provides a complete, feasible and optimal academic schedule that satisfies medium cum hard constraints for the Federal University of Petroleum Resource Effurun in Delta State of Nigeria using memetic algorithm. Results showed that model converges after 4minutes and 29seconds; while its convergence time depends on use of belief space to ensure agents do not violate model bounds, encoding scheme used amongst others. The schedule considers both instructors and students’ preference as medium constraints of high priority.

Memetic computing through bio-inspired heuristics integration with sequential quadratic programming for nonlinear systems arising in different physical models.

BackgroundIn this study, bio-inspired computing is exploited for solving system of nonlinear equations using variants of genetic algorithms (GAs) as a tool for global search method hybrid with sequential quadratic programming (SQP) for efficient local search. The fitness function is constructed by defining the error function for systems of nonlinear equations in mean square sense. The design parameters of mathematical models are trained by exploiting the competency of GAs and refinement are carried out by viable SQP algorithm.ResultsTwelve versions of the memetic approach GA-SQP are designed by taking a different set of reproduction routines in the optimization process. Performance of proposed variants is evaluated on six numerical problems comprising of system of nonlinear equations arising in the interval arithmetic benchmark model, kinematics, neurophysiology, combustion and chemical equilibrium. Comparative studies of the proposed results in terms of accuracy, convergence and complexity are performed with the help of statistical performance indices to establish the worth of the schemes.ConclusionsAccuracy and convergence of the memetic computing GA-SQP is found better in each case of the simulation study and effectiveness of the scheme is further established through results of statistics based on different performance indices for accuracy and complexity.

A two-objective memetic approach for the node localization problem in wireless sensor networks

Wireless sensor networks (WSNs) are emerging as an efficient way to sense the physical phenomenon without the need of wired links and spending huge money on sensor devices. In WSNs, finding the accurate locations of sensor nodes is essential since the location inaccuracy makes the collected data fruitless. In this paper, we propose a two-objective memetic approach called the Three Phase Memetic Approach that finds the locations of sensor nodes with high accuracy. The proposed algorithm is composed of three operators (phases). The first phase, which is a combination of three node-estimating approaches, is used to provide good starting locations for sensor nodes. The second and third phases are then utilized for mitigating the localization errors in the first operator. To test the proposed algorithm, we compare it with the simulated annealing-based localization algorithm, genetic algorithm-based localization, Particle Swarm Optimization-based Localization algorithm, trilateration-based simulated annealing algorithm, imperialist competitive algorithm and Pareto Archived Evolution Strategy on ten randomly created and four specific network topologies with four different values of transmission ranges. The comparisons indicate that the proposed algorithm outperforms the other algorithms in terms of the coordinate estimations of sensor nodes.

A memetic approach to vehicle routing problem with dynamic requests

The paper presents an effective algorithm for solving Vehicle Routing Problem with Dynamic Requests based on memetic algorithms. The proposed method is applied to a widely-used set of 21 benchmark problems yielding 14 new best-know results when using the same numbers of fitness function evaluations as the comparative methods. Apart from encouraging numerical outcomes, the main contribution of the paper is investigation into the importance of the so-called starting delay parameter, whose appropriate selection has a crucial impact on the quality of results. Another key factor in accomplishing high quality results is attributed to the proposed effective mechanism of knowledge transfer between partial solutions developed in consecutive time slices. While particular problem encoding and memetic local optimization scheme were already presented in the literature, the novelty of this work lies in their innovative combination into one synergetic system as well as their application to a different problem than in the original works.

Memetic electromagnetism algorithm for surface reconstruction with rational bivariate Bernstein basis functions

Surface reconstruction is a very important issue with outstanding applications in fields such as medical imaging (computer tomography, magnetic resonance), biomedical engineering (customized prosthesis and medical implants), computer-aided design and manufacturing (reverse engineering for the automotive, aerospace and shipbuilding industries), rapid prototyping (scale models of physical parts from CAD data), computer animation and film industry (motion capture, character modeling), archaeology (digital representation and storage of archaeological sites and assets), virtual/augmented reality, and many others. In this paper we address the surface reconstruction problem by using rational Bezier surfaces. This problem is by far more complex than the case for curves we solved in a previous paper. In addition, we deal with data points subjected to measurement noise and irregular sampling, replicating the usual conditions of real-world applications. Our method is based on a memetic approach combining a powerful metaheuristic method for global optimization (the electromagnetism algorithm) with a local search method. This method is applied to a benchmark of five illustrative examples exhibiting challenging features. Our experimental results show that the method performs very well, and it can recover the underlying shape of surfaces with very good accuracy.

Preserving Privacy in Health Care Information: A Memetic Approach

Preserving Privacy in Health Care Information: A Memetic Approach

Constructing cultural memory: A memetic approach

Abstract Research on memes has been growing within cultural memory studies. To date, this research has largely focused on the ways in which memes transmit representations of shared pasts. This article, instead, examines how memes are used to construct cultural memory. It makes two arguments: first, that cultural memory can be engineered through memes and can take the form of texts, artefacts or practices. Second, large sets of such memes – ‘memeplexes’ – can help societies not only engage in acts of remembrance, but also help guide their future behaviours and attitudes. However, these processes are problematic, and the article discusses these tensions. The Nobel Peace Prize serves as the article’s case study, with an analysis of the Nobel Peace Prize Ceremony illustrating its arguments.

Computational investigation of simple memetic approaches for continuous global optimization

In Locatelli et al. (2014) [20] a memetic approach, called MDE (Memetic Differential Evolution), for the solution of continuous global optimization problems, has been introduced and proved to be quite efficient in spite of its simplicity. In this paper we computationally investigate some variants of MDE. The investigation reveals that the best tested variant of MDE outperforms the original MDE itself, but also that the best variant depends on some properties of the function to be optimized. In particular, a greedy variant of MDE turns out to perform very well over functions with a single-funnel landscape, while another variant, based on a diversity measure applied to the members of the population, works better over functions with a multi-funnel landscape. A hybrid approach is also proposed which combines both the previous variants in order to obtain an overall performance which is good over all functions.

Memetic Music Composition

Computers and artificial intelligence play a key role in the production of artwork through the designing of synthetic agents that are able to reproduce the capabilities of human artists in assembling high-quality artefacts such as paintings and sculptures. In this context, music composition represents one of the art disciplines that can greatly benefit from the appropriate use of computational intelligence, as witnessed by the large number of research activities performed in this field over the recent years. Nevertheless, the automatic composition of music is far from being completely and precisely perfected due to the intrinsic virtuosity that characterizes human musicians’ capabilities. This paper reduces this gap with the proposal of an intelligent scheme for the efficient composition of melodies based on a musical method that is inspired by and strongly characterized by human virtuosity: the unfigured bass technique. In particular, we formulate this music composition technique as an optimization problem and solve it with an adaptive multiagent memetic approach comprising diverse metaheuristics, the composer agents that cooperate to create high-quality four-voice pieces of music starting from a bass line as input. A collection of experimental studies on the famous Bach’s four-voice chorales showed that the cooperation among different optimization strategies yields improved performance over the solutions obtained by conventional and hybrid evolutionary algorithms.

Evolutionary approach to violating group anonymity using third-party data.

In the era of Big Data, it is almost impossible to completely restrict access to primary non-aggregated statistical data. However, risk of violating privacy of individual respondents and groups of respondents by analyzing primary data has not been reduced. There is a need in developing subtler methods of data protection to come to grips with these challenges. In some cases, individual and group privacy can be easily violated, because the primary data contain attributes that uniquely identify individuals and groups thereof. Removing such attributes from the dataset is a crude solution and does not guarantee complete privacy. In the field of providing individual data anonymity, this problem has been widely recognized, and various methods have been proposed to solve it. In the current work, we demonstrate that it is possible to violate group anonymity as well, even if those attributes that uniquely identify the group are removed. As it turns out, it is possible to use third-party data to build a fuzzy model of a group. Typically, such a model comes in a form of a set of fuzzy rules, which can be used to determine membership grades of respondents in the group with a level of certainty sufficient to violate group anonymity. In the work, we introduce an evolutionary computing based method to build such a model. We also discuss a memetic approach to protecting the data from group anonymity violation in this case.

¿Son válidos los argumentos evolutivos desacreditadores de la memética contra la racionalidad del teísmo?

The aim of this paper is to answer the question of whether the two evolutionary debunking arguments (EDA) which, in my interpretation, can be developed from memetics (the argument of contagion and the argument of belief in belief) successfully undermine both the internalist and the externalist rationality of theism. The EDA against the rationality of religious belief try to prove that if such belief is caused by processes that do not track the truth, then this belief is unwarranted or even false. I will argue that the contagion argument is not valid due to the theoretical weakness of memetics, while the argument of belief in belief may be partly valid, since it is logically independent of the memetic approach. Finally, I will examine a possible refutation of that argument based on the thesis that the propositional belief in God is not decisive for the adaptive explanation of the complex system of religion. I will show, briefly, that this does not save theism from another EDA based on the adaptive nature of religion.

A memetic algorithm for cardinality-constrained portfolio optimization with transaction costs

A memetic approach that combines a genetic algorithm (GA) and quadratic programming is used to address the problem of optimal portfolio selection with cardinality constraints and piecewise linear transaction costs. The framework used is an extension of the standard Markowitz mean–variance model that incorporates realistic constraints, such as upper and lower bounds for investment in individual assets and/or groups of assets, and minimum trading restrictions. The inclusion of constraints that limit the number of assets in the final portfolio and piecewise linear transaction costs transforms the selection of optimal portfolios into a mixed-integer quadratic problem, which cannot be solved by standard optimization techniques. We propose to use a genetic algorithm in which the candidate portfolios are encoded using a set representation to handle the combinatorial aspect of the optimization problem. Besides specifying which assets are included in the portfolio, this representation includes attributes that encode the trading operation (sell/hold/buy) performed when the portfolio is rebalanced. The results of this hybrid method are benchmarked against a range of investment strategies (passive management, the equally weighted portfolio, the minimum variance portfolio, optimal portfolios without cardinality constraints, ignoring transaction costs or obtained with L1 regularization) using publicly available data. The transaction costs and the cardinality constraints provide regularization mechanisms that generally improve the out-of-sample performance of the selected portfolios.

Memetic Harmony Search Algorithm Based on Multi-objective Differ-ential Evolution of Evolving Spiking Neural Networks

Spiking neural network (SNN) plays an essential role in classification problems. Although there are many models of SNN, Evolving Spiking Neural Network (ESNN) is widely used in many recent research works. Evolutionary algorithms, mainly differential evolution (DE) have been used for enhancing ESNN algorithm. However, many realworld optimization problems include several contradictory objectives. Rather than single optimization, Multi-Objective Optimization (MOO) can be utilized as a set of optimal solutions to solve these problems. In this paper, Harmony Search (HS) and memetic approach was used to improve the performance of MOO with ESNN. Consequently, Memetic Harmony Search Multi-Objective Differential Evolution with Evolving Spiking Neural Network (MEHSMODEESNN) was applied to improve ESNN structure and accuracy rates. Standard data sets from the UCI machine learning are used for evaluating the performance of this enhanced multi objective hybrid model. The experimental results have proved that the Memetic Harmony Search Multi-Objective Differential Evolution with Evolving Spiking Neural Network (MEHSMODE-ESNN) gives better results in terms of accuracy and network structure.

Parameter Optimization of Single-Diode Model of Photovoltaic Cell Using Memetic Algorithm

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.

Religia w perspektywie ewolucjonistycznej, ze szczególnym uwzględnieniem jej funkcji socjalizacyjnej

This paper gives an overview of the potential integrative function of religion from the evolutionary standpoint. In order to fulfill those aims, the work first addresses the characterization of the use of an evolutionary paradigm for the study of religion. In this section, the most prominent evolutionary hypotheses of religion are discussed: the sociobiological view, memetic approach and cognitive perspective. Subsequently, the article also introduces several conceptual difficulties of the whole program of Darwinian religious study. In the final part of the paper, a couple of the most important religious mechanisms of social integration are shown.

Integrated management systems for enhancing education for sustainable development in universities: a memetic approach

There is a need for new approaches for enhancing education for sustainable development in universities. Memetics, which is about effective pathways of communication, could be such a new, promising approach. Quality assurance is required in universities to secure and improve education, which could be another approach. The aim of this study is to look into whether and how frameworks for processes and procedures for quality assurance, such as management systems, could be utilized to promote higher education for sustainable development. The study approaches this from both a theoretical and a practical standpoint. An evolutionary perspective was chosen, considering higher education for sustainable development and management systems as memes, or basic units of cultural replication. The practical context was studied by looking into how 11 universities in the Nordic countries have enhanced ESD with management systems. We found that both higher education for sustainable development and management systems could be considered successful memes and that management systems could be applied to enhance higher education for sustainable development.

Evolutionary Approaches for the Extraction of Classification Rules

This paper provides evolutionary approaches in order to extract comprehensible and accurate classification rules. Indeed to construct a model of classification tone must extract not only accurate rules but comprehensible also, to help the human interpretation of the model and the decision make process. In this paper the authors describe a purely genetic approach, then a tabu search approach and finaly a memetic algorithm to extract classification rules. The memetic approach is a hybridization of a genetic algorithm (GA) and a local search based on a tabu search algorithm. Knowing that the amount of treated data is always huge in data mining applications, the authors propose to decrease the running time of the GA using a parallel scheme. In the authors' scheme the concept of generation has been removed and replaced by the cycle one and each individual owns a lifespan represented by a number of cycles affected to it randomly at its birth and at the end of which it disappears from the population. Consequently, only certain individuals of the population are evaluated within each iteration of the algorithm and not all our heterogeneous population. This causes the substantial reduction of the total running time of the algorithm since the evaluations of all individuals of each generation necessitates more than 80% of the total running time of a classical GA. This approach has been developed with the goal to present a new and efficient parallel scheme of the classical GA with better performances in terms of running time.

A Memetic Approach for Improving Minimum Cost of Economic Load Dispatch Problems

Economic load dispatch problem is a popular optimization problem in electrical power system field, which has been so far tackled by various mathematical and metaheuristic approaches including Lagrangian relaxation, branch and bound method, genetic algorithm, tabu search, particle swarm optimization, harmony search, and Taguchi method. On top of these techniques, this study proposes a novel memetic algorithm scheme combining metaheuristic algorithm and gradient-based technique to find better solutions for an economic load dispatch problem with valve-point loading. Because metaheuristic algorithms have the strength in global search and gradient-based techniques have the strength in local search, the combination approach obtains better results than those of any single approach. A bench-mark example of 40 generating-unit economic load dispatch problem demonstrates that the memetic approach can further improve the existing best solutions from the literature.