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

PubMed

Raja, Muhammad Asif Zahoor; Kiani, Adiqa Kausar; Shehzad, Azam; Zameer, Aneela

2016-01-01

In 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. Twelve 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. Accuracy 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.

Electricity Load Forecasting Using Support Vector Regression with Memetic Algorithms

PubMed Central

Hu, Zhongyi; Xiong, Tao

2013-01-01

Electricity load forecasting is an important issue that is widely explored and examined in power systems operation literature and commercial transactions in electricity markets literature as well. Among the existing forecasting models, support vector regression (SVR) has gained much attention. Considering the performance of SVR highly depends on its parameters; this study proposed a firefly algorithm (FA) based memetic algorithm (FA-MA) to appropriately determine the parameters of SVR forecasting model. In the proposed FA-MA algorithm, the FA algorithm is applied to explore the solution space, and the pattern search is used to conduct individual learning and thus enhance the exploitation of FA. Experimental results confirm that the proposed FA-MA based SVR model can not only yield more accurate forecasting results than the other four evolutionary algorithms based SVR models and three well-known forecasting models but also outperform the hybrid algorithms in the related existing literature. PMID:24459425

Electricity load forecasting using support vector regression with memetic algorithms.

PubMed

Hu, Zhongyi; Bao, Yukun; Xiong, Tao

2013-01-01

Electricity load forecasting is an important issue that is widely explored and examined in power systems operation literature and commercial transactions in electricity markets literature as well. Among the existing forecasting models, support vector regression (SVR) has gained much attention. Considering the performance of SVR highly depends on its parameters; this study proposed a firefly algorithm (FA) based memetic algorithm (FA-MA) to appropriately determine the parameters of SVR forecasting model. In the proposed FA-MA algorithm, the FA algorithm is applied to explore the solution space, and the pattern search is used to conduct individual learning and thus enhance the exploitation of FA. Experimental results confirm that the proposed FA-MA based SVR model can not only yield more accurate forecasting results than the other four evolutionary algorithms based SVR models and three well-known forecasting models but also outperform the hybrid algorithms in the related existing literature.

Compression of next-generation sequencing quality scores using memetic algorithm

PubMed Central

2014-01-01

Background The exponential growth of next-generation sequencing (NGS) derived DNA data poses great challenges to data storage and transmission. Although many compression algorithms have been proposed for DNA reads in NGS data, few methods are designed specifically to handle the quality scores. Results In this paper we present a memetic algorithm (MA) based NGS quality score data compressor, namely MMQSC. The algorithm extracts raw quality score sequences from FASTQ formatted files, and designs compression codebook using MA based multimodal optimization. The input data is then compressed in a substitutional manner. Experimental results on five representative NGS data sets show that MMQSC obtains higher compression ratio than the other state-of-the-art methods. Particularly, MMQSC is a lossless reference-free compression algorithm, yet obtains an average compression ratio of 22.82% on the experimental data sets. Conclusions The proposed MMQSC compresses NGS quality score data effectively. It can be utilized to improve the overall compression ratio on FASTQ formatted files. PMID:25474747

Classification-Assisted Memetic Algorithms for Equality-Constrained Optimization Problems

NASA Astrophysics Data System (ADS)

Handoko, Stephanus Daniel; Kwoh, Chee Keong; Ong, Yew Soon

Regressions has successfully been incorporated into memetic algorithm (MA) to build surrogate models for the objective or constraint landscape of optimization problems. This helps to alleviate the needs for expensive fitness function evaluations by performing local refinements on the approximated landscape. Classifications can alternatively be used to assist MA on the choice of individuals that would experience refinements. Support-vector-assisted MA were recently proposed to alleviate needs for function evaluations in the inequality-constrained optimization problems by distinguishing regions of feasible solutions from those of the infeasible ones based on some past solutions such that search efforts can be focussed on some potential regions only. For problems having equality constraints, however, the feasible space would obviously be extremely small. It is thus extremely difficult for the global search component of the MA to produce feasible solutions. Hence, the classification of feasible and infeasible space would become ineffective. In this paper, a novel strategy to overcome such limitation is proposed, particularly for problems having one and only one equality constraint. The raw constraint value of an individual, instead of its feasibility class, is utilized in this work.

Memetic Algorithm-Based Multi-Objective Coverage Optimization for Wireless Sensor Networks

PubMed Central

Chen, Zhi; Li, Shuai; Yue, Wenjing

2014-01-01

Maintaining effective coverage and extending the network lifetime as much as possible has become one of the most critical issues in the coverage of WSNs. In this paper, we propose a multi-objective coverage optimization algorithm for WSNs, namely MOCADMA, which models the coverage control of WSNs as the multi-objective optimization problem. MOCADMA uses a memetic algorithm with a dynamic local search strategy to optimize the coverage of WSNs and achieve the objectives such as high network coverage, effective node utilization and more residual energy. In MOCADMA, the alternative solutions are represented as the chromosomes in matrix form, and the optimal solutions are selected through numerous iterations of the evolution process, including selection, crossover, mutation, local enhancement, and fitness evaluation. The experiment and evaluation results show MOCADMA can have good capabilities in maintaining the sensing coverage, achieve higher network coverage while improving the energy efficiency and effectively prolonging the network lifetime, and have a significant improvement over some existing algorithms. PMID:25360579

Memetic algorithm-based multi-objective coverage optimization for wireless sensor networks.

PubMed

Chen, Zhi; Li, Shuai; Yue, Wenjing

2014-10-30

Maintaining effective coverage and extending the network lifetime as much as possible has become one of the most critical issues in the coverage of WSNs. In this paper, we propose a multi-objective coverage optimization algorithm for WSNs, namely MOCADMA, which models the coverage control of WSNs as the multi-objective optimization problem. MOCADMA uses a memetic algorithm with a dynamic local search strategy to optimize the coverage of WSNs and achieve the objectives such as high network coverage, effective node utilization and more residual energy. In MOCADMA, the alternative solutions are represented as the chromosomes in matrix form, and the optimal solutions are selected through numerous iterations of the evolution process, including selection, crossover, mutation, local enhancement, and fitness evaluation. The experiment and evaluation results show MOCADMA can have good capabilities in maintaining the sensing coverage, achieve higher network coverage while improving the energy efficiency and effectively prolonging the network lifetime, and have a significant improvement over some existing algorithms.

Time series modeling and forecasting using memetic algorithms for regime-switching models.

PubMed

Bergmeir, Christoph; Triguero, Isaac; Molina, Daniel; Aznarte, José Luis; Benitez, José Manuel

2012-11-01

In this brief, we present a novel model fitting procedure for the neuro-coefficient smooth transition autoregressive model (NCSTAR), as presented by Medeiros and Veiga. The model is endowed with a statistically founded iterative building procedure and can be interpreted in terms of fuzzy rule-based systems. The interpretability of the generated models and a mathematically sound building procedure are two very important properties of forecasting models. The model fitting procedure employed by the original NCSTAR is a combination of initial parameter estimation by a grid search procedure with a traditional local search algorithm. We propose a different fitting procedure, using a memetic algorithm, in order to obtain more accurate models. An empirical evaluation of the method is performed, applying it to various real-world time series originating from three forecasting competitions. The results indicate that we can significantly enhance the accuracy of the models, making them competitive to models commonly used in the field.

A Memetic Algorithm for Global Optimization of Multimodal Nonseparable Problems.

PubMed

Zhang, Geng; Li, Yangmin

2016-06-01

It is a big challenging issue of avoiding falling into local optimum especially when facing high-dimensional nonseparable problems where the interdependencies among vector elements are unknown. In order to improve the performance of optimization algorithm, a novel memetic algorithm (MA) called cooperative particle swarm optimizer-modified harmony search (CPSO-MHS) is proposed in this paper, where the CPSO is used for local search and the MHS for global search. The CPSO, as a local search method, uses 1-D swarm to search each dimension separately and thus converges fast. Besides, it can obtain global optimum elements according to our experimental results and analyses. MHS implements the global search by recombining different vector elements and extracting global optimum elements. The interaction between local search and global search creates a set of local search zones, where global optimum elements reside within the search space. The CPSO-MHS algorithm is tested and compared with seven other optimization algorithms on a set of 28 standard benchmarks. Meanwhile, some MAs are also compared according to the results derived directly from their corresponding references. The experimental results demonstrate a good performance of the proposed CPSO-MHS algorithm in solving multimodal nonseparable problems.

Multi-Objective Community Detection Based on Memetic Algorithm

PubMed Central

2015-01-01

Community detection has drawn a lot of attention as it can provide invaluable help in understanding the function and visualizing the structure of networks. Since single objective optimization methods have intrinsic drawbacks to identifying multiple significant community structures, some methods formulate the community detection as multi-objective problems and adopt population-based evolutionary algorithms to obtain multiple community structures. Evolutionary algorithms have strong global search ability, but have difficulty in locating local optima efficiently. In this study, in order to identify multiple significant community structures more effectively, a multi-objective memetic algorithm for community detection is proposed by combining multi-objective evolutionary algorithm with a local search procedure. The local search procedure is designed by addressing three issues. Firstly, nondominated solutions generated by evolutionary operations and solutions in dominant population are set as initial individuals for local search procedure. Then, a new direction vector named as pseudonormal vector is proposed to integrate two objective functions together to form a fitness function. Finally, a network specific local search strategy based on label propagation rule is expanded to search the local optimal solutions efficiently. The extensive experiments on both artificial and real-world networks evaluate the proposed method from three aspects. Firstly, experiments on influence of local search procedure demonstrate that the local search procedure can speed up the convergence to better partitions and make the algorithm more stable. Secondly, comparisons with a set of classic community detection methods illustrate the proposed method can find single partitions effectively. Finally, the method is applied to identify hierarchical structures of networks which are beneficial for analyzing networks in multi-resolution levels. PMID:25932646

Multi-objective community detection based on memetic algorithm.

PubMed

Wu, Peng; Pan, Li

2015-01-01

Community detection has drawn a lot of attention as it can provide invaluable help in understanding the function and visualizing the structure of networks. Since single objective optimization methods have intrinsic drawbacks to identifying multiple significant community structures, some methods formulate the community detection as multi-objective problems and adopt population-based evolutionary algorithms to obtain multiple community structures. Evolutionary algorithms have strong global search ability, but have difficulty in locating local optima efficiently. In this study, in order to identify multiple significant community structures more effectively, a multi-objective memetic algorithm for community detection is proposed by combining multi-objective evolutionary algorithm with a local search procedure. The local search procedure is designed by addressing three issues. Firstly, nondominated solutions generated by evolutionary operations and solutions in dominant population are set as initial individuals for local search procedure. Then, a new direction vector named as pseudonormal vector is proposed to integrate two objective functions together to form a fitness function. Finally, a network specific local search strategy based on label propagation rule is expanded to search the local optimal solutions efficiently. The extensive experiments on both artificial and real-world networks evaluate the proposed method from three aspects. Firstly, experiments on influence of local search procedure demonstrate that the local search procedure can speed up the convergence to better partitions and make the algorithm more stable. Secondly, comparisons with a set of classic community detection methods illustrate the proposed method can find single partitions effectively. Finally, the method is applied to identify hierarchical structures of networks which are beneficial for analyzing networks in multi-resolution levels.

Multi-Objective Memetic Search for Robust Motion and Distortion Correction in Diffusion MRI.

PubMed

Hering, Jan; Wolf, Ivo; Maier-Hein, Klaus H

2016-10-01

Effective image-based artifact correction is an essential step in the analysis of diffusion MR images. Many current approaches are based on retrospective registration, which becomes challenging in the realm of high b -values and low signal-to-noise ratio, rendering the corresponding correction schemes more and more ineffective. We propose a novel registration scheme based on memetic search optimization that allows for simultaneous exploitation of different signal intensity relationships between the images, leading to more robust registration results. We demonstrate the increased robustness and efficacy of our method on simulated as well as in vivo datasets. In contrast to the state-of-art methods, the median target registration error (TRE) stayed below the voxel size even for high b -values (3000 s ·mm -2 and higher) and low SNR conditions. We also demonstrate the increased precision in diffusion-derived quantities by evaluating Neurite Orientation Dispersion and Density Imaging (NODDI) derived measures on a in vivo dataset with severe motion artifacts. These promising results will potentially inspire further studies on metaheuristic optimization in diffusion MRI artifact correction and image registration in general.

An ILP based memetic algorithm for finding minimum positive influence dominating sets in social networks

NASA Astrophysics Data System (ADS)

Lin, Geng; Guan, Jian; Feng, Huibin

2018-06-01

The positive influence dominating set problem is a variant of the minimum dominating set problem, and has lots of applications in social networks. It is NP-hard, and receives more and more attention. Various methods have been proposed to solve the positive influence dominating set problem. However, most of the existing work focused on greedy algorithms, and the solution quality needs to be improved. In this paper, we formulate the minimum positive influence dominating set problem as an integer linear programming (ILP), and propose an ILP based memetic algorithm (ILPMA) for solving the problem. The ILPMA integrates a greedy randomized adaptive construction procedure, a crossover operator, a repair operator, and a tabu search procedure. The performance of ILPMA is validated on nine real-world social networks with nodes up to 36,692. The results show that ILPMA significantly improves the solution quality, and is robust.

Facing the phase problem in Coherent Diffractive Imaging via Memetic Algorithms.

PubMed

Colombo, Alessandro; Galli, Davide Emilio; De Caro, Liberato; Scattarella, Francesco; Carlino, Elvio

2017-02-09

Coherent Diffractive Imaging is a lensless technique that allows imaging of matter at a spatial resolution not limited by lens aberrations. This technique exploits the measured diffraction pattern of a coherent beam scattered by periodic and non-periodic objects to retrieve spatial information. The diffracted intensity, for weak-scattering objects, is proportional to the modulus of the Fourier Transform of the object scattering function. Any phase information, needed to retrieve its scattering function, has to be retrieved by means of suitable algorithms. Here we present a new approach, based on a memetic algorithm, i.e. a hybrid genetic algorithm, to face the phase problem, which exploits the synergy of deterministic and stochastic optimization methods. The new approach has been tested on simulated data and applied to the phasing of transmission electron microscopy coherent electron diffraction data of a SrTiO 3 sample. We have been able to quantitatively retrieve the projected atomic potential, and also image the oxygen columns, which are not directly visible in the relevant high-resolution transmission electron microscopy images. Our approach proves to be a new powerful tool for the study of matter at atomic resolution and opens new perspectives in those applications in which effective phase retrieval is necessary.

A POPULATION MEMETICS APPROACH TO CULTURAL EVOLUTION IN CHAFFINCH SONG: DIFFERENTIATION AMONG POPULATIONS.

PubMed

Lynch, Alejandro; Baker, Allan J

1994-04-01

We investigated cultural evolution in populations of common chaffinches (Fringilla coelebs) in the Atlantic islands (Azores, Madeira, and Canaries) and neighboring continental regions (Morocco and Iberia) by employing a population-memetic approach. To quantify differentiation, we used the concept of a song meme, defined as a single syllable or a series of linked syllables capable of being transmitted. The levels of cultural differentiation are higher among the Canaries populations than among the Azorean ones, even though the islands are on average closer to each other geographically. This is likely the result of reduced levels of migration, lower population sizes, and bottlenecks (possibly during the colonization of these populations) in the Canaries; all these factors produce a smaller effective population size and therefore accentuate the effects of differentiation by random drift. Significant levels of among-population differentiation in the Azores, in spite of substantial levels of migration, attest to the differentiating effects of high mutation rates of memes, which allow the accumulation of new mutants in different populations before migration can disperse them throughout the entire region. © 1994 The Society for the Study of Evolution.

A new memetic algorithm for mitigating tandem automated guided vehicle system partitioning problem

NASA Astrophysics Data System (ADS)

Pourrahimian, Parinaz

2017-11-01

Automated Guided Vehicle System (AGVS) provides the flexibility and automation demanded by Flexible Manufacturing System (FMS). However, with the growing concern on responsible management of resource use, it is crucial to manage these vehicles in an efficient way in order reduces travel time and controls conflicts and congestions. This paper presents the development process of a new Memetic Algorithm (MA) for optimizing partitioning problem of tandem AGVS. MAs employ a Genetic Algorithm (GA), as a global search, and apply a local search to bring the solutions to a local optimum point. A new Tabu Search (TS) has been developed and combined with a GA to refine the newly generated individuals by GA. The aim of the proposed algorithm is to minimize the maximum workload of the system. After all, the performance of the proposed algorithm is evaluated using Matlab. This study also compared the objective function of the proposed MA with GA. The results showed that the TS, as a local search, significantly improves the objective function of the GA for different system sizes with large and small numbers of zone by 1.26 in average.

Advanced fitness landscape analysis and the performance of memetic algorithms.

PubMed

Merz, Peter

2004-01-01

Memetic algorithms (MAs) have demonstrated very effective in combinatorial optimization. This paper offers explanations as to why this is so by investigating the performance of MAs in terms of efficiency and effectiveness. A special class of MAs is used to discuss efficiency and effectiveness for local search and evolutionary meta-search. It is shown that the efficiency of MAs can be increased drastically with the use of domain knowledge. However, effectiveness highly depends on the structure of the problem. As is well-known, identifying this structure is made easier with the notion of fitness landscapes: the local properties of the fitness landscape strongly influence the effectiveness of the local search while the global properties strongly influence the effectiveness of the evolutionary meta-search. This paper also introduces new techniques for analyzing the fitness landscapes of combinatorial problems; these techniques focus on the investigation of random walks in the fitness landscape starting at locally optimal solutions as well as on the escape from the basins of attractions of current local optima. It is shown for NK-landscapes and landscapes of the unconstrained binary quadratic programming problem (BQP) that a random walk to another local optimum can be used to explain the efficiency of recombination in comparison to mutation. Moreover, the paper shows that other aspects like the size of the basins of attractions of local optima are important for the efficiency of MAs and a local search escape analysis is proposed. These simple analysis techniques have several advantages over previously proposed statistical measures and provide valuable insight into the behaviour of MAs on different kinds of landscapes.

An effective PSO-based memetic algorithm for flow shop scheduling.

PubMed

Liu, Bo; Wang, Ling; Jin, Yi-Hui

2007-02-01

This paper proposes an effective particle swarm optimization (PSO)-based memetic algorithm (MA) for the permutation flow shop scheduling problem (PFSSP) with the objective to minimize the maximum completion time, which is a typical non-deterministic polynomial-time (NP) hard combinatorial optimization problem. In the proposed PSO-based MA (PSOMA), both PSO-based searching operators and some special local searching operators are designed to balance the exploration and exploitation abilities. In particular, the PSOMA applies the evolutionary searching mechanism of PSO, which is characterized by individual improvement, population cooperation, and competition to effectively perform exploration. On the other hand, the PSOMA utilizes several adaptive local searches to perform exploitation. First, to make PSO suitable for solving PFSSP, a ranked-order value rule based on random key representation is presented to convert the continuous position values of particles to job permutations. Second, to generate an initial swarm with certain quality and diversity, the famous Nawaz-Enscore-Ham (NEH) heuristic is incorporated into the initialization of population. Third, to balance the exploration and exploitation abilities, after the standard PSO-based searching operation, a new local search technique named NEH_1 insertion is probabilistically applied to some good particles selected by using a roulette wheel mechanism with a specified probability. Fourth, to enrich the searching behaviors and to avoid premature convergence, a simulated annealing (SA)-based local search with multiple different neighborhoods is designed and incorporated into the PSOMA. Meanwhile, an effective adaptive meta-Lamarckian learning strategy is employed to decide which neighborhood to be used in SA-based local search. Finally, to further enhance the exploitation ability, a pairwise-based local search is applied after the SA-based search. Simulation results based on benchmarks demonstrate the effectiveness

An Abstract Process and Metrics Model for Evaluating Unified Command and Control: A Scenario and Technology Agnostic Approach

DTIC Science & Technology

2004-06-01

18 EBO Cognitive or Memetic input type ..................................................................... 18 Unanticipated EBO generated... Memetic Effects Based COA.................................................................................... 23 Policy...41 Belief systems or Memetic Content Metrics

University Course Timetabling with Probability Collectives

DTIC Science & Technology

2008-03-01

as other problems such as scheduling hospital shifts for nurses. The authors of [15] detail the use of a memetic algorithm. A memetic uses local...Heuristics, vol. 9, pp. 451-470, 2003. [15] E. K. Burke, J. P. Newall and R. F. Weare, "A memetic algorithm for university exam timetabling," in

Facility Reliability and Maintainability: An Investigation of the Air Force Civil Engineering Recurring Work Program

DTIC Science & Technology

1989-09-01

18:2). A recent survey by the Strategic Air Command (SAC) Mechanical Fquipment Management Evaluation Team ( MEMET ) determined that equipment was...identified by MEMET included Maintenance Action Sheets (MAS) that reported work which was not completed, and other MAS which annotated recurring work...readily apparent. Problem Military. The Deputy Chief of Staff for Engineering and Services, HQ SAC, established the MEMET in 1984 in response to a

The Grand Challenges of Command and Control Policy

DTIC Science & Technology

2006-06-01

Memetic Warfare Memes are ideas that can be modeled and simulated. In a modern journalistic environment, dynamic information feedback from the theater...output type such that both adversarial meme processes and our counter anti- memetic activity could be modeled, simulated, and assessed. I am now...opposing force of the consequence of using biological or chemical weapons on the invading American forces. Do we have the proper memetic dynamics

Identification of HVAC (Heating, Ventilating, and Air Conditioning) Deficiencies Using Analysis of Job Order Data

DTIC Science & Technology

1989-09-01

Maintenance Evaluation Team ( MEMET ), stated, in his booklet To Aspire For Excellence, the need for emphasis on product- oriented performance (3:17). Existing...JOAGE - Job Order/Facility Age JOSF - Job Order/Facility Square Feet LSD - Least Significant Difference MEMET - Mechanical Equipment Maintenance

HELPR: Hybrid Evolutionary Learning for Pattern Recognition

DTIC Science & Technology

2005-12-01

to a new approach called memetic algorithms that combines machine learning systems with human expertise to create new tools that have the advantage...architecture could form the foundation for a memetic system capable of solving ATR problems faster and more accurately than possible using pure human expertise

Obstetrics at Decisive Crossroads Regarding Pattern-Recognition of Fetal Heart Rate Decelerations: Scientific Principles and Lessons From Memetics.

PubMed

Sholapurkar, Shashikant L

2018-04-01

The survival of cardiotocography (CTG) as a tool for intrapartum fetal monitoring seems threatened somewhat unjustifiably and unwittingly despite the absence of better alternatives. Fetal heart rate (FHR) decelerations are center-stage (most important) in the interpretation of CTG with maximum impact on three-tier classification. The pattern-discrimination of FHR decelerations is inexorably linked to their nomenclature. Unscientific or flawed nomenclature of decelerations can explain the dysfunctional CTG interpretation leading to errors in detection of acidemic fetuses. There are three contrasting concepts about categorization of FHR decelerations: 1) all rapid decelerations (the vast majority) should be grouped as "variable" because they are predominantly due to cord-compression, 2) all decelerations are due to chemoreflex from fetal hypoxemia hence their timing is not important, and 3) FHR decelerations should be categorized into "early/late/variable" based primarily on their time relationship to contractions. These theoretical concepts are like memes (ideas/beliefs). Lessons from "memetics" are that the most popular, attractive or established beliefs may not necessarily be true, scientific, beneficial or even without harm. Decelerations coincident with contractions with trough corresponding to the peak of contractions cannot be explained by cord-compression or increasing hypoxia (from compromised uteroplacental perfusion, cord-compression or even cerebral hypoperfusion/anoxia purportedly conceivable from head-compression). Decelerations due to hypoxemia would be associated with delayed recovery of decelerations (lag phase). It is a scientific imperative to cast away disproven/falsified theories. Practices based on unscientific theories lead to patient harm. Clinicians should urgently adopt the categorization of FHR decelerations based primarily of the time relationship to contractions as originally proposed by Hon and Caldeyro-Barcia. This analytical review

Cultural variation in savannah sparrow, Passerculus sandwichensis, songs: an analysis using the meme concept.

PubMed

Burnell

1998-10-01

I used the meme concept to investigate patterns of cultural variation among the songs of eight, geographically distinct populations of savannah sparrows. Memes composed of only one syllable were geographically widespread and randomly distributed among populations, but memes of two-, three- and four-syllables became progressively more restricted in their geographical distribution. Thus, the populations were memetically more similar with respect to one-syllable memes and more divergent with respect to larger memes. These results suggest that differences in memetic mutation rates and susceptibility to loss by memetic drift could be sufficient to create the observed pattern of greater divergence among populations for large memes. Copyright 1998 The Association for the Study of Animal Behaviour.

Projectile Roll Dynamics and Control With a Low-Cost Skid-to-Turn Maneuver System

DTIC Science & Technology

2013-03-01

scheme. The mechatronics of the maneuver system was provided. The suitability of this design for survival at gun launch was assessed through...Projectile Roll Dynamics and Control With a Low-Cost Skid-to-Turn Maneuver System by Frank Fresconi, Ilmars Celmins, Mark Ilg, and James...5069 ARL-TR-6363 March 2013 Projectile Roll Dynamics and Control With a Low-Cost Skid-to-Turn Maneuver System Frank Fresconi, Ilmars

A new distributed systems scheduling algorithm: a swarm intelligence approach

NASA Astrophysics Data System (ADS)

Haghi Kashani, Mostafa; Sarvizadeh, Raheleh; Jameii, Mahdi

2011-12-01

The scheduling problem in distributed systems is known as an NP-complete problem, and methods based on heuristic or metaheuristic search have been proposed to obtain optimal and suboptimal solutions. The task scheduling is a key factor for distributed systems to gain better performance. In this paper, an efficient method based on memetic algorithm is developed to solve the problem of distributed systems scheduling. With regard to load balancing efficiently, Artificial Bee Colony (ABC) has been applied as local search in the proposed memetic algorithm. The proposed method has been compared to existing memetic-Based approach in which Learning Automata method has been used as local search. The results demonstrated that the proposed method outperform the above mentioned method in terms of communication cost.

The Dynamics of Change: Regeneration of the Indonesian Army.

DTIC Science & Technology

1984-11-15

Army Special Forces (KOPASSANDHA): COI1U11a11Idl MG Yogic Suardi Memet * it(B) CCL, W ismuy Ar i.-j MU11,1IL101 A (N4) Deputy Commander *MG Soedjasmin0...KLTG Yogic Suardi Memet Cdr KODAM, VI/Cdr Special Forces Cdr KOWILHAN II BG S. Momon HI. Ad ip)Utro) Unknown Chi Army F iulaaic L’uia’ BC IP1

Ant Colony Optimization With Local Search for Dynamic Traveling Salesman Problems.

PubMed

Mavrovouniotis, Michalis; Muller, Felipe M; Yang, Shengxiang

2016-06-13

For a dynamic traveling salesman problem (DTSP), the weights (or traveling times) between two cities (or nodes) may be subject to changes. Ant colony optimization (ACO) algorithms have proved to be powerful methods to tackle such problems due to their adaptation capabilities. It has been shown that the integration of local search operators can significantly improve the performance of ACO. In this paper, a memetic ACO algorithm, where a local search operator (called unstring and string) is integrated into ACO, is proposed to address DTSPs. The best solution from ACO is passed to the local search operator, which removes and inserts cities in such a way that improves the solution quality. The proposed memetic ACO algorithm is designed to address both symmetric and asymmetric DTSPs. The experimental results show the efficiency of the proposed memetic algorithm for addressing DTSPs in comparison with other state-of-the-art algorithms.

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

PubMed

Jan, Steven

2018-01-01

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.

“The Two Brothers”: Reconciling Perceptual-Cognitive and Statistical Models of Musical Evolution

PubMed Central

Jan, Steven

2018-01-01

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. PMID:29670551

Development of a Tool for an Efficient Calibration of CORSIM Models

DOT National Transportation Integrated Search

2014-08-01

This project proposes a Memetic Algorithm (MA) for the calibration of microscopic traffic flow simulation models. The proposed MA includes a combination of genetic and simulated annealing algorithms. The genetic algorithm performs the exploration of ...

Delineating Cultural Models

DTIC Science & Technology

2010-08-25

Behavioral Decision Making, 22(2), 191-208. doi:10.1002/bdm.621 Blackmore, S. (1998). Imitation and the definition of a meme. Journal of Memetics...Maat and order in African cosmology : A conceptual tool for understanding indigenous knowledge. Journal of Black Studies, 38(6), 951-967. doi

Why We All Want It to Work: Towards a Culturally Based Model for Technology and Educational Change

ERIC Educational Resources Information Center

Kerr, Stephen T.

2005-01-01

This paper explores reasons why the use of technology in education may be so attractive to so many people. Two emerging perspectives--memetics, and the social history of technology--are explored, and a typology of technology-as-cultural-tool is presented. Finally, implications of these ideas for educational change are considered.

On the Optimization of Sentence Imitation in Primary School English Teaching from the Perspective of Strong Memes

ERIC Educational Resources Information Center

Lin, Wang

2017-01-01

A sentence is an important unit in English language, and plays a crucial role in language teaching and learning as well. For many years, sentence teaching is always worth discussion in English teaching, because sentence imitation is very important for students' construction of logical discourse. This paper, based on memetics, proposes some certain…

Experimental Flight Characterization of a Canard-Controlled, Subsonic Missile

DTIC Science & Technology

2017-08-01

ARL-TR-8086 ● AUG 2017 US Army Research Laboratory Experimental Flight Characterization of a Canard- Controlled , Subsonic Missile...Laboratory Experimental Flight Characterization of a Canard- Controlled , Subsonic Missile by Frank Fresconi, Ilmars Celmins, James Maley, and...valid OMB control number. PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ADDRESS. 1. REPORT DATE (DD-MM-YYYY) August 2017 2. REPORT TYPE Technical

Experimental Flight Characterization of Spin Stabilized Projectiles at High Angle of Attack

DTIC Science & Technology

2017-08-07

ARL-TR-8082 ● AUG 2017 US Army Research Laboratory Experimental Flight Characterization of Spin- Stabilized Projectiles at High ...Experimental Flight Characterization of Spin- Stabilized Projectiles at High Angle of Attack by Frank Fresconi and Ilmars Celmins Weapons and Materials...June 2016–June 2017 4. TITLE AND SUBTITLE Experimental Flight Characterization of Spin-Stabilized Projectiles at High Angle of Attack 5a. CONTRACT

Immersive Simulation of Complex Social Environments

DTIC Science & Technology

2008-12-01

Complexity, 7, 18–30. Dawkins , R., 1989: The Selfish Gene (2nd ed.). New York: Oxford University Press. Dennett, D. C., 1995: Darwin’s Dangerous...interpretation, bias, and misinformation, which create erroneous versions of what has transpired. Dawkins presents a model for describing knowledge...evolution within a social group through interpersonal exchange (memetics). ( Dawkins , 1987) Where genetic duplication tends to be precise (and mutation

The mystery of altruism and transcultural nursing.

PubMed

Dowd, Steven; Davidhizar, Ruth; Giger, Joyce Newman

2007-01-01

Why do some individuals choose the professions they do? Is it for altruistic reasons? This article examines this question from the standpoints of sociobiology, evolutionary biology, game theory, and memetics. Implications for transcultural nursing are included. The Giger-Davidhizar Transcultural Assessment Model is presented as a nursing model and might explain altruism even beyond other models. An overview of the Giger-Davidhizar Transcultural Assessment Model is included.

An introduction to the COLIN optimization interface.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hart, William Eugene

2003-03-01

We describe COLIN, a Common Optimization Library INterface for C++. COLIN provides C++ template classes that define a generic interface for both optimization problems and optimization solvers. COLIN is specifically designed to facilitate the development of hybrid optimizers, for which one optimizer calls another to solve an optimization subproblem. We illustrate the capabilities of COLIN with an example of a memetic genetic programming solver.

An Improved Memetic Algorithm for Break Scheduling

NASA Astrophysics Data System (ADS)

Widl, Magdalena; Musliu, Nysret

In this paper we consider solving a complex real life break scheduling problem. This problem of high practical relevance arises in many working areas, e.g. in air traffic control and other fields where supervision personnel is working. The objective is to assign breaks to employees such that various constraints reflecting legal demands or ergonomic criteria are satisfied and staffing requirement violations are minimised.

Memetic Algorithms, Domain Knowledge, and Financial Investing

ERIC Educational Resources Information Center

Du, Jie

2012-01-01

While the question of how to use human knowledge to guide evolutionary search is long-recognized, much remains to be done to answer this question adequately. This dissertation aims to further answer this question by exploring the role of domain knowledge in evolutionary computation as applied to real-world, complex problems, such as financial…

Machine Learning Based Dimensionality Reduction Facilitates Ligand Diffusion Paths Assessment: A Case of Cytochrome P450cam.

PubMed

Rydzewski, J; Nowak, W

2016-04-12

In this work we propose an application of a nonlinear dimensionality reduction method to represent the high-dimensional configuration space of the ligand-protein dissociation process in a manner facilitating interpretation. Rugged ligand expulsion paths are mapped into 2-dimensional space. The mapping retains the main structural changes occurring during the dissociation. The topological similarity of the reduced paths may be easily studied using the Fréchet distances, and we show that this measure facilitates machine learning classification of the diffusion pathways. Further, low-dimensional configuration space allows for identification of residues active in transport during the ligand diffusion from a protein. The utility of this approach is illustrated by examination of the configuration space of cytochrome P450cam involved in expulsing camphor by means of enhanced all-atom molecular dynamics simulations. The expulsion trajectories are sampled and constructed on-the-fly during molecular dynamics simulations using the recently developed memetic algorithms [ Rydzewski, J.; Nowak, W. J. Chem. Phys. 2015 , 143 ( 12 ), 124101 ]. We show that the memetic algorithms are effective for enforcing the ligand diffusion and cavity exploration in the P450cam-camphor complex. Furthermore, we demonstrate that machine learning techniques are helpful in inspecting ligand diffusion landscapes and provide useful tools to examine structural changes accompanying rare events.

Agent Based Evidence Marshaling: Discovery-Based Enhancement Tools for C2 Systems

DTIC Science & Technology

2003-12-01

www5conf.inria.fr/fich_html/papers/P5/Overview.html, accessed on 1/15/2001. Dawkins , R., The Selfish Gene , Oxford University Press, Oxford, UK, 1989. Eco, U...Charles S. Peirce and Richard Dawkins argued that ideas can be alive and propagated through human life. Dawkins called these living ideas memes... Dawkins , 1989], while Peirce characterized them as “substantial things” [Buchler, 1955, 340]. This concept and the study of memetics that accompanies it

Memetics--A Growth Industry in US Military Operations

DTIC Science & Technology

2006-01-01

Ret) and Schmitt John F., presentation to MCCDC, September, 2005. 2 Dawkins , R. (1976), “The Selfish Gene ,” Oxford University Press, Oxford 1...NY: Atlantic Monthly Press; 1995. Brodie, Richard, “Virus of the Mind: The New Science of the Meme” Dawkins , R. (1976), “The Selfish Gene ...interpersonal and societal interaction. Richard Dawkins introduced 1 Van Riper, Paul K., LtGen USMC

The Idea of Physical Education: A Memetic Perspective

ERIC Educational Resources Information Center

Tinning, Richard

2012-01-01

Background: Despite dire predictions of its demise, physical education continues to survive across most countries of the world. Moreover, the form of its survival is remarkably similar across cultures. Why has physical education survived as a cultural practice and why is its form so similar given the marked differences that exist between many…

On uncertainty in information and ignorance in knowledge

NASA Astrophysics Data System (ADS)

Ayyub, Bilal M.

2010-05-01

This paper provides an overview of working definitions of knowledge, ignorance, information and uncertainty and summarises formalised philosophical and mathematical framework for their analyses. It provides a comparative examination of the generalised information theory and the generalised theory of uncertainty. It summarises foundational bases for assessing the reliability of knowledge constructed as a collective set of justified true beliefs. It discusses system complexity for ancestor simulation potentials. It offers value-driven communication means of knowledge and contrarian knowledge using memes and memetics.

Enabling the extended compact genetic algorithm for real-parameter optimization by using adaptive discretization.

PubMed

Chen, Ying-ping; Chen, Chao-Hong

2010-01-01

An adaptive discretization method, called split-on-demand (SoD), enables estimation of distribution algorithms (EDAs) for discrete variables to solve continuous optimization problems. SoD randomly splits a continuous interval if the number of search points within the interval exceeds a threshold, which is decreased at every iteration. After the split operation, the nonempty intervals are assigned integer codes, and the search points are discretized accordingly. As an example of using SoD with EDAs, the integration of SoD and the extended compact genetic algorithm (ECGA) is presented and numerically examined. In this integration, we adopt a local search mechanism as an optional component of our back end optimization engine. As a result, the proposed framework can be considered as a memetic algorithm, and SoD can potentially be applied to other memetic algorithms. The numerical experiments consist of two parts: (1) a set of benchmark functions on which ECGA with SoD and ECGA with two well-known discretization methods: the fixed-height histogram (FHH) and the fixed-width histogram (FWH) are compared; (2) a real-world application, the economic dispatch problem, on which ECGA with SoD is compared to other methods. The experimental results indicate that SoD is a better discretization method to work with ECGA. Moreover, ECGA with SoD works quite well on the economic dispatch problem and delivers solutions better than the best known results obtained by other methods in existence.

The trouble with memes : Inference versus imitation in cultural creation.

PubMed

Atran, S

2001-12-01

Memes are hypothetical cultural units passed on by imitation; although nonbiological, they undergo Darwinian selection like genes. Cognitive study of multimodular human minds undermines memetics: unlike in genetic replication, high-fidelity transmission of cultural information is the exception, not the rule. Constant, rapid "mutation" of information during communication generates endlessly varied creations that nevertheless adhere to modular input conditions. The sort of cultural information most susceptible to modular processing is that most readily acquired by children, most easily transmitted across individuals, most apt to survive within a culture, most likely to recur in different cultures, and most disposed to cultural variation and elaboration.

Hybridization of decomposition and local search for multiobjective optimization.

PubMed

Ke, Liangjun; Zhang, Qingfu; Battiti, Roberto

2014-10-01

Combining ideas from evolutionary algorithms, decomposition approaches, and Pareto local search, this paper suggests a simple yet efficient memetic algorithm for combinatorial multiobjective optimization problems: memetic algorithm based on decomposition (MOMAD). It decomposes a combinatorial multiobjective problem into a number of single objective optimization problems using an aggregation method. MOMAD evolves three populations: 1) population P(L) for recording the current solution to each subproblem; 2) population P(P) for storing starting solutions for Pareto local search; and 3) an external population P(E) for maintaining all the nondominated solutions found so far during the search. A problem-specific single objective heuristic can be applied to these subproblems to initialize the three populations. At each generation, a Pareto local search method is first applied to search a neighborhood of each solution in P(P) to update P(L) and P(E). Then a single objective local search is applied to each perturbed solution in P(L) for improving P(L) and P(E), and reinitializing P(P). The procedure is repeated until a stopping condition is met. MOMAD provides a generic hybrid multiobjective algorithmic framework in which problem specific knowledge, well developed single objective local search and heuristics and Pareto local search methods can be hybridized. It is a population based iterative method and thus an anytime algorithm. Extensive experiments have been conducted in this paper to study MOMAD and compare it with some other state-of-the-art algorithms on the multiobjective traveling salesman problem and the multiobjective knapsack problem. The experimental results show that our proposed algorithm outperforms or performs similarly to the best so far heuristics on these two problems.

Five Misunderstandings About Cultural Evolution.

PubMed

Henrich, Joseph; Boyd, Robert; Richerson, Peter J

2008-06-01

Recent debates about memetics have revealed some widespread misunderstandings about Darwinian approaches to cultural evolution. Drawing from these debates, this paper disputes five common claims: (1) mental representations are rarely discrete, and therefore models that assume discrete, gene-like particles (i.e., replicators) are useless; (2) replicators are necessary for cumulative, adaptive evolution; (3) content-dependent psychological biases are the only important processes that affect the spread of cultural representations; (4) the "cultural fitness" of a mental representation can be inferred from its successful transmission; and (5) selective forces only matter if the sources of variation are random. We close by sketching the outlines of a unified evolutionary science of culture.

Behavioral similarity measurement based on image processing for robots that use imitative learning

NASA Astrophysics Data System (ADS)

Sterpin B., Dante G.; Martinez S., Fernando; Jacinto G., Edwar

2017-02-01

In the field of the artificial societies, particularly those are based on memetics, imitative behavior is essential for the development of cultural evolution. Applying this concept for robotics, through imitative learning, a robot can acquire behavioral patterns from another robot. Assuming that the learning process must have an instructor and, at least, an apprentice, the fact to obtain a quantitative measurement for their behavioral similarity, would be potentially useful, especially in artificial social systems focused on cultural evolution. In this paper the motor behavior of both kinds of robots, for two simple tasks, is represented by 2D binary images, which are processed in order to measure their behavioral similarity. The results shown here were obtained comparing some similarity measurement methods for binary images.

Exploring the application of an evolutionary educational complex systems framework to teaching and learning about issues in the science and technology classroom

NASA Astrophysics Data System (ADS)

Yoon, Susan Anne

Understanding the world through a complex systems lens has recently garnered a great deal of interest in many knowledge disciplines. In the educational arena, interactional studies, through their focus on understanding patterns of system behaviour including the dynamical processes and trajectories of learning, lend support for investigating how a complex systems approach can inform educational research. This study uses previously existing literature and tools for complex systems applications and seeks to extend this research base by exploring learning outcomes of a complex systems framework when applied to curriculum and instruction. It is argued that by applying the evolutionary dynamics of variation, interaction and selection, complexity may be harnessed to achieve growth in both the social and cognitive systems of the classroom. Furthermore, if the goal of education, i.e., the social system under investigation, is to teach for understanding, conceptual knowledge of the kind described in Popper's (1972; 1976) World 3, needs to evolve. Both the study of memetic processes and knowledge building pioneered by Bereiter (cf. Bereiter, 2002) draw on the World 3 notion of ideas existing as conceptual artifacts that can be investigated as products outside of the individual mind providing an educational lens from which to proceed. The curricular topic addressed is the development of an ethical understanding of the scientific and technological issues of genetic engineering. 11 grade 8 students are studied as they proceed through 40 hours of curricular instruction based on the complex systems evolutionary framework. Results demonstrate growth in both complex systems thinking and content knowledge of the topic of genetic engineering. Several memetic processes are hypothesized to have influenced how and why ideas change. Categorized by factors influencing either reflective or non-reflective selection, these processes appear to have exerted differential effects on students

The natural selection of organizational and safety culture within a small to medium sized enterprise (SME).

PubMed

Brooks, Benjamin

2008-01-01

Small to Medium Sized Enterprises (SMEs) form the majority of Australian businesses. This study uses ethnographic research methods to describe the organizational culture of a small furniture-manufacturing business in southern Australia. Results show a range of cultural assumptions variously 'embedded' within the enterprise. In line with memetics - Richard Dawkin's cultural application of Charles Darwin's theory of Evolution by Natural Selection, the author suggests that these assumptions compete to be replicated and retained within the organization. The author suggests that dominant assumptions are naturally selected, and that the selection can be better understood by considering the cultural assumptions in reference to Darwin's original principles and Frederik Barth's anthropological framework of knowledge. The results are discussed with reference to safety systems, negative cultural elements called Cultural Safety Viruses, and how our understanding of this particular organizational culture might be used to build resistance to these viruses.

An investigation into the use of color as a device to convey memes during the Little Ice Age

NASA Astrophysics Data System (ADS)

White, Peter A.

Color is used as a tool in visual communication to express ideas in a symbolic fashion. It can also be used as a guide to assist the viewer in the visual narrative. Artwork created in the period of time between 1300 to 1850 in northern and central Europe provides a comprehensive perspective in the use of color as symbol and color as an elucidative devise. This period of time is known as the Little Ice Age, the duration of which spans European history between the Medieval period and the Romantic era. The extreme climatic conditions of this era caused profound changes in society on many levels and influenced the use of color in paintings throughout this chapter in history. The new paradigm of the science of ideas, called memetics, provides a framework to analyze the expression of ideas through the use of color within this span of time.

A self-adaptive memeplexes robust search scheme for solving stochastic demands vehicle routing problem

NASA Astrophysics Data System (ADS)

Chen, Xianshun; Feng, Liang; Ong, Yew Soon

2012-07-01

In this article, we proposed a self-adaptive memeplex robust search (SAMRS) for finding robust and reliable solutions that are less sensitive to stochastic behaviours of customer demands and have low probability of route failures, respectively, in vehicle routing problem with stochastic demands (VRPSD). In particular, the contribution of this article is three-fold. First, the proposed SAMRS employs the robust solution search scheme (RS 3) as an approximation of the computationally intensive Monte Carlo simulation, thus reducing the computation cost of fitness evaluation in VRPSD, while directing the search towards robust and reliable solutions. Furthermore, a self-adaptive individual learning based on the conceptual modelling of memeplex is introduced in the SAMRS. Finally, SAMRS incorporates a gene-meme co-evolution model with genetic and memetic representation to effectively manage the search for solutions in VRPSD. Extensive experimental results are then presented for benchmark problems to demonstrate that the proposed SAMRS serves as an efficable means of generating high-quality robust and reliable solutions in VRPSD.

A novel metaheuristic for continuous optimization problems: Virus optimization algorithm

NASA Astrophysics Data System (ADS)

Liang, Yun-Chia; Rodolfo Cuevas Juarez, Josue

2016-01-01

A novel metaheuristic for continuous optimization problems, named the virus optimization algorithm (VOA), is introduced and investigated. VOA is an iteratively population-based method that imitates the behaviour of viruses attacking a living cell. The number of viruses grows at each replication and is controlled by an immune system (a so-called 'antivirus') to prevent the explosive growth of the virus population. The viruses are divided into two classes (strong and common) to balance the exploitation and exploration effects. The performance of the VOA is validated through a set of eight benchmark functions, which are also subject to rotation and shifting effects to test its robustness. Extensive comparisons were conducted with over 40 well-known metaheuristic algorithms and their variations, such as artificial bee colony, artificial immune system, differential evolution, evolutionary programming, evolutionary strategy, genetic algorithm, harmony search, invasive weed optimization, memetic algorithm, particle swarm optimization and simulated annealing. The results showed that the VOA is a viable solution for continuous optimization.

Bringing Darwin into the social sciences and the humanities: cultural evolution and its philosophical implications.

PubMed

Blancke, Stefaan; Denis, Gilles

2018-04-10

In the field of cultural evolution it is generally assumed that the study of culture and cultural change would benefit enormously from being informed by evolutionary thinking. Recently, however, there has been much debate about what this "being informed" means. According to the standard view, an interesting analogy obtains between cultural and biological evolution. In the literature, however, the analogy is interpreted and used in at least three distinct, but interrelated ways. We provide a taxonomy in order to clarify these different meanings. Subsequently, we discuss the alternatives model of cultural attraction theory and memetics, which both challenge basic assumptions of the standard view. Finally, we briefly summarize the contributions to the special issue on Darwin in the Humanities and the Social Sciences, which is the result of a collaborative project between scholars and scientists from the universities of Lille and Ghent. Furthermore, we explain how they add to the discussions about the integration of evolutionary thinking and the study of culture.

A Novel Clustering Methodology Based on Modularity Optimisation for Detecting Authorship Affinities in Shakespearean Era Plays

PubMed Central

Craig, Hugh; Berretta, Regina; Moscato, Pablo

2016-01-01

In this study we propose a novel, unsupervised clustering methodology for analyzing large datasets. This new, efficient methodology converts the general clustering problem into the community detection problem in graph by using the Jensen-Shannon distance, a dissimilarity measure originating in Information Theory. Moreover, we use graph theoretic concepts for the generation and analysis of proximity graphs. Our methodology is based on a newly proposed memetic algorithm (iMA-Net) for discovering clusters of data elements by maximizing the modularity function in proximity graphs of literary works. To test the effectiveness of this general methodology, we apply it to a text corpus dataset, which contains frequencies of approximately 55,114 unique words across all 168 written in the Shakespearean era (16th and 17th centuries), to analyze and detect clusters of similar plays. Experimental results and comparison with state-of-the-art clustering methods demonstrate the remarkable performance of our new method for identifying high quality clusters which reflect the commonalities in the literary style of the plays. PMID:27571416

Multi-strategy coevolving aging particle optimization.

PubMed

Iacca, Giovanni; Caraffini, Fabio; Neri, Ferrante

2014-02-01

We propose Multi-Strategy Coevolving Aging Particles (MS-CAP), a novel population-based algorithm for black-box optimization. In a memetic fashion, MS-CAP combines two components with complementary algorithm logics. In the first stage, each particle is perturbed independently along each dimension with a progressively shrinking (decaying) radius, and attracted towards the current best solution with an increasing force. In the second phase, the particles are mutated and recombined according to a multi-strategy approach in the fashion of the ensemble of mutation strategies in Differential Evolution. The proposed algorithm is tested, at different dimensionalities, on two complete black-box optimization benchmarks proposed at the Congress on Evolutionary Computation 2010 and 2013. To demonstrate the applicability of the approach, we also test MS-CAP to train a Feedforward Neural Network modeling the kinematics of an 8-link robot manipulator. The numerical results show that MS-CAP, for the setting considered in this study, tends to outperform the state-of-the-art optimization algorithms on a large set of problems, thus resulting in a robust and versatile optimizer.

Memes and their themata.

PubMed

Miranker, Willard L

2010-01-01

When it is instantiated as a neuronal state, a meme is characterized as a phenotype in a novel neuronal sense. A thema is an instantiation of a meme as a conscious experience (a thought-meme). It is a primitive to which no location may be attributed, and it serves as a canonical representative of a class of memes. Memes in such a class may have physical or ideal (Platonic) instantiations. Pairing of this memetic phenotype characterization with the ideal thematic primitive is an example of other pairings in nature that are identified, and in particular it informs a description of the pairing of the unconscious mind and manifestations of consciousness. Interrelationship of these pairings is what illuminates aspects of each of them. These constructs support introduction of a consciousness thesis and then a notion of a dynamic self-referential grammar that generates a growing repertoire of consciousness manifestations. A method showing how a neuronal state generates a specific concept (thema) is introduced, and a sample of a class of examples is given. Pointers to experiments relevant to development of the thesis are given.

The evolution of replicators.

PubMed Central

Szathmáry, E

2000-01-01

Replicators of interest in chemistry, biology and culture are briefly surveyed from a conceptual point of view. Systems with limited heredity have only a limited evolutionary potential because the number of available types is too low. Chemical cycles, such as the formose reaction, are holistic replicators since replication is not based on the successive addition of modules. Replicator networks consisting of catalytic molecules (such as reflexively autocatalytic sets of proteins, or reproducing lipid vesicles) are hypothetical ensemble replicators, and their functioning rests on attractors of their dynamics. Ensemble replicators suffer from the paradox of specificity: while their abstract feasibility seems to require a high number of molecular types, the harmful effect of side reactions calls for a small system size. No satisfactory solution to this problem is known. Phenotypic replicators do not pass on their genotypes, only some aspects of the phenotype are transmitted. Phenotypic replicators with limited heredity include genetic membranes, prions and simple memetic systems. Memes in human culture are unlimited hereditary, phenotypic replicators, based on language. The typical path of evolution goes from limited to unlimited heredity, and from attractor-based to modular (digital) replicators. PMID:11127914

The evolution of replicators.

PubMed

Szathmáry, E

2000-11-29

Replicators of interest in chemistry, biology and culture are briefly surveyed from a conceptual point of view. Systems with limited heredity have only a limited evolutionary potential because the number of available types is too low. Chemical cycles, such as the formose reaction, are holistic replicators since replication is not based on the successive addition of modules. Replicator networks consisting of catalytic molecules (such as reflexively autocatalytic sets of proteins, or reproducing lipid vesicles) are hypothetical ensemble replicators, and their functioning rests on attractors of their dynamics. Ensemble replicators suffer from the paradox of specificity: while their abstract feasibility seems to require a high number of molecular types, the harmful effect of side reactions calls for a small system size. No satisfactory solution to this problem is known. Phenotypic replicators do not pass on their genotypes, only some aspects of the phenotype are transmitted. Phenotypic replicators with limited heredity include genetic membranes, prions and simple memetic systems. Memes in human culture are unlimited hereditary, phenotypic replicators, based on language. The typical path of evolution goes from limited to unlimited heredity, and from attractor-based to modular (digital) replicators.

Obstetrics at Decisive Crossroads Regarding Pattern-Recognition of Fetal Heart Rate Decelerations: Scientific Principles and Lessons From Memetics

PubMed Central

Sholapurkar, Shashikant L.

2018-01-01

The survival of cardiotocography (CTG) as a tool for intrapartum fetal monitoring seems threatened somewhat unjustifiably and unwittingly despite the absence of better alternatives. Fetal heart rate (FHR) decelerations are center-stage (most important) in the interpretation of CTG with maximum impact on three-tier classification. The pattern-discrimination of FHR decelerations is inexorably linked to their nomenclature. Unscientific or flawed nomenclature of decelerations can explain the dysfunctional CTG interpretation leading to errors in detection of acidemic fetuses. There are three contrasting concepts about categorization of FHR decelerations: 1) all rapid decelerations (the vast majority) should be grouped as “variable” because they are predominantly due to cord-compression, 2) all decelerations are due to chemoreflex from fetal hypoxemia hence their timing is not important, and 3) FHR decelerations should be categorized into “early/late/variable” based primarily on their time relationship to contractions. These theoretical concepts are like memes (ideas/beliefs). Lessons from “memetics” are that the most popular, attractive or established beliefs may not necessarily be true, scientific, beneficial or even without harm. Decelerations coincident with contractions with trough corresponding to the peak of contractions cannot be explained by cord-compression or increasing hypoxia (from compromised uteroplacental perfusion, cord-compression or even cerebral hypoperfusion/anoxia purportedly conceivable from head-compression). Decelerations due to hypoxemia would be associated with delayed recovery of decelerations (lag phase). It is a scientific imperative to cast away disproven/falsified theories. Practices based on unscientific theories lead to patient harm. Clinicians should urgently adopt the categorization of FHR decelerations based primarily of the time relationship to contractions as originally proposed by Hon and Caldeyro-Barcia. This analytical review shows it to be underpinned by most robust physiological and scientific hypotheses unlike the other categorizations associated with untruthful hypotheses, irreconcilable fallacies and contradictions. Without truthful framework and meaningful pattern-recognition of FHR decelerations, the CTG will not fulfil its true potential. PMID:29511418

Mapping the Conformation Space of Wildtype and Mutant H-Ras with a Memetic, Cellular, and Multiscale Evolutionary Algorithm

PubMed Central

Clausen, Rudy; Ma, Buyong; Nussinov, Ruth; Shehu, Amarda

2015-01-01

An important goal in molecular biology is to understand functional changes upon single-point mutations in proteins. Doing so through a detailed characterization of structure spaces and underlying energy landscapes is desirable but continues to challenge methods based on Molecular Dynamics. In this paper we propose a novel algorithm, SIfTER, which is based instead on stochastic optimization to circumvent the computational challenge of exploring the breadth of a protein’s structure space. SIfTER is a data-driven evolutionary algorithm, leveraging experimentally-available structures of wildtype and variant sequences of a protein to define a reduced search space from where to efficiently draw samples corresponding to novel structures not directly observed in the wet laboratory. The main advantage of SIfTER is its ability to rapidly generate conformational ensembles, thus allowing mapping and juxtaposing landscapes of variant sequences and relating observed differences to functional changes. We apply SIfTER to variant sequences of the H-Ras catalytic domain, due to the prominent role of the Ras protein in signaling pathways that control cell proliferation, its well-studied conformational switching, and abundance of documented mutations in several human tumors. Many Ras mutations are oncogenic, but detailed energy landscapes have not been reported until now. Analysis of SIfTER-computed energy landscapes for the wildtype and two oncogenic variants, G12V and Q61L, suggests that these mutations cause constitutive activation through two different mechanisms. G12V directly affects binding specificity while leaving the energy landscape largely unchanged, whereas Q61L has pronounced, starker effects on the landscape. An implementation of SIfTER is made available at http://www.cs.gmu.edu/~ashehu/?q=OurTools. We believe SIfTER is useful to the community to answer the question of how sequence mutations affect the function of a protein, when there is an abundance of experimental structures that can be exploited to reconstruct an energy landscape that would be computationally impractical to do via Molecular Dynamics. PMID:26325505

Introducing the Future Now: Using Memetics and Popular Culture to Identify the Post-9/11 Homeland Security Zeitgeist

DTIC Science & Technology

2008-03-01

the case within NCIS. Law and Order: Criminal Intent Episode 30 February 27, 2007 A reporter is poisoned with Polonium - 210 and the FBI joins the... effect did the terrorist attacks on September 11, 2001 have on American culture? One outcome was the emergence of “homeland security” as a new...ABSTRACT What effect did the terrorist attacks on September 11, 2001, have on American culture? One outcome was the emergence of “homeland security” as

Diabetes and Ramadan: An Update On Use of Glycemic Therapies During Fasting

PubMed Central

Ahmed, Mohamed H.; Abdu, Tarig A. M.

2011-01-01

The fasting of Ramadan is observed by a large proportion of Muslims with diabetes. Recommendations for the management of diabetes during Ramadan were last published in 2005 by the American Diabetes Association. Several studies in this field have since been published, some addressing the use of new pharmacological agents in managing diabetes during Ramadan. The incritin memetics are potentially safe during Ramadan; the DPP4 inhibitors vildagliptin and sitagliptin provide an effective and safe therapeutic option, administered either alone or in combination with metformin or sulfonylureas. There are no published studies on the use of GLP-1 receptor agonists during Ramadan. Among the sulfonylureas, gliclazide MR (modified release) and glimepride can be safely used during Ramadan, but glibenclamide should be avoided due to the associated risk of hypoglycemia. In selected patients with type 1 and type 2 diabetes, the long-acting insulin analogues glargine and detemir, as well as the premixed insulin analogues, can be used with minimal risk of metabolic derangement or hypoglycemia; the risk is higher in type 1 diabetes. Insulin pumps can potentially empower patients with diabetes and enable safe fasting during the month of Ramadan. Further clinical trials are needed to evaluate the safety and efficacy of new antidiabetic agents and new diabetes-related technologies during Ramadan. PMID:21727749

Diabetes and Ramadan: an update on use of glycemic therapies during fasting.

PubMed

Ahmed, Mohamed H; Abdu, Tarig A M

2011-01-01

The fasting of Ramadan is observed by a large proportion of Muslims with diabetes. Recommendations for the management of diabetes during Ramadan were last published in 2005 by the American Diabetes Association. Several studies in this field have since been published, some addressing the use of new pharmacological agents in managing diabetes during Ramadan. The incritin memetics are potentially safe during Ramadan; the DPP4 inhibitors vildagliptin and sitagliptin provide an effective and safe therapeutic option, administered either alone or in combination with metformin or sulfonylureas. There are no published studies on the use of GLP-1 receptor agonists during Ramadan. Among the sulfonylureas, gliclazide MR (modified release) and glimepride can be safely used during Ramadan, but glibenclamide should be avoided due to the associated risk of hypoglycemia. In selected patients with type 1 and type 2 diabetes, the long-acting insulin analogues glargine and detemir, as well as the premixed insulin analogues, can be used with minimal risk of metabolic derangement or hypoglycemia; the risk is higher in type 1 diabetes. Insulin pumps can potentially empower patients with diabetes and enable safe fasting during the month of Ramadan. Further clinical trials are needed to evaluate the safety and efficacy of new antidiabetic agents and new diabetes-related technologies during Ramadan.

Using Memes and Memetic Processes to Explain Social and Conceptual Influences on Student Understanding about Complex Socio-Scientific Issues

ERIC Educational Resources Information Center

Yoon, Susan

2008-01-01

This study investigated seventh grade learners' decision making about genetic engineering concepts and applications. A social network analyses supported by technology tracked changes in student understanding with a focus on social and conceptual influences. Results indicated that several social and conceptual mechanisms potentially affected how…

Hybrid Self-Adaptive Evolution Strategies Guided by Neighborhood Structures for Combinatorial Optimization Problems.

PubMed

Coelho, V N; Coelho, I M; Souza, M J F; Oliveira, T A; Cota, L P; Haddad, M N; Mladenovic, N; Silva, R C P; Guimarães, F G

2016-01-01

This article presents an Evolution Strategy (ES)--based algorithm, designed to self-adapt its mutation operators, guiding the search into the solution space using a Self-Adaptive Reduced Variable Neighborhood Search procedure. In view of the specific local search operators for each individual, the proposed population-based approach also fits into the context of the Memetic Algorithms. The proposed variant uses the Greedy Randomized Adaptive Search Procedure with different greedy parameters for generating its initial population, providing an interesting exploration-exploitation balance. To validate the proposal, this framework is applied to solve three different [Formula: see text]-Hard combinatorial optimization problems: an Open-Pit-Mining Operational Planning Problem with dynamic allocation of trucks, an Unrelated Parallel Machine Scheduling Problem with Setup Times, and the calibration of a hybrid fuzzy model for Short-Term Load Forecasting. Computational results point out the convergence of the proposed model and highlight its ability in combining the application of move operations from distinct neighborhood structures along the optimization. The results gathered and reported in this article represent a collective evidence of the performance of the method in challenging combinatorial optimization problems from different application domains. The proposed evolution strategy demonstrates an ability of adapting the strength of the mutation disturbance during the generations of its evolution process. The effectiveness of the proposal motivates the application of this novel evolutionary framework for solving other combinatorial optimization problems.

Big data for big questions: it is time for data analysts to act

PubMed Central

Moscato, Pablo

2015-01-01

Pablo Moscato speaks to Francesca Lake, Managing Editor Australian Research Council Future Fellow Prof. Pablo Moscato was born in 1964 in La Plata, Argentina. Obtaining his B.Sc. in Physics at University of La Plata, his PhD was defended at UNICAMP, Brazil. While at the California Institute of Technology Concurrent Computation Program he developed, in collaboration with Michael Norman, the first application of a methodology later called ‘memetic algorithms’, which is now widely used internationally. He is the founding co-director of the Priority Research Centre for Bioinformatics, Biomarker Discovery and Information-based Medicine (CIBM) (2006–present) and the funding director of the Newcastle Bioinformatics Initiative (2002–2006) of The University of Newcastle (Australia). He is also Chief Investigator of the Australian Research Council Centre in Bioinformatics. He is one of Australia's most cited computer scientists. Over the past 7 years, he has introduced a unifying hallmark of cancer progression based on the changes of information theory quantifiers, and developed a novel mathematical model and an associated solution procedure based on combinatorial optimization techniques to identify drug combinations for cancer therapeutics. In addition, he has identified proteomic signatures to predict the clinical symptoms of Alzheimer's disease, among other ‘firsts’. He is a member of the Editorial Board of Future Science OA. PMID:28031895

Computational intelligence-based polymerase chain reaction primer selection based on a novel teaching-learning-based optimisation.

PubMed

Cheng, Yu-Huei

2014-12-01

Specific primers play an important role in polymerase chain reaction (PCR) experiments, and therefore it is essential to find specific primers of outstanding quality. Unfortunately, many PCR constraints must be simultaneously inspected which makes specific primer selection difficult and time-consuming. This paper introduces a novel computational intelligence-based method, Teaching-Learning-Based Optimisation, to select the specific and feasible primers. The specified PCR product lengths of 150-300 bp and 500-800 bp with three melting temperature formulae of Wallace's formula, Bolton and McCarthy's formula and SantaLucia's formula were performed. The authors calculate optimal frequency to estimate the quality of primer selection based on a total of 500 runs for 50 random nucleotide sequences of 'Homo species' retrieved from the National Center for Biotechnology Information. The method was then fairly compared with the genetic algorithm (GA) and memetic algorithm (MA) for primer selection in the literature. The results show that the method easily found suitable primers corresponding with the setting primer constraints and had preferable performance than the GA and the MA. Furthermore, the method was also compared with the common method Primer3 according to their method type, primers presentation, parameters setting, speed and memory usage. In conclusion, it is an interesting primer selection method and a valuable tool for automatic high-throughput analysis. In the future, the usage of the primers in the wet lab needs to be validated carefully to increase the reliability of the method.

Delamination detection using methods of computational intelligence

NASA Astrophysics Data System (ADS)

Ihesiulor, Obinna K.; Shankar, Krishna; Zhang, Zhifang; Ray, Tapabrata

2012-11-01

Abstract Reliable delamination prediction scheme is indispensable in order to prevent potential risks of catastrophic failures in composite structures. The existence of delaminations changes the vibration characteristics of composite laminates and hence such indicators can be used to quantify the health characteristics of laminates. An approach for online health monitoring of in-service composite laminates is presented in this paper that relies on methods based on computational intelligence. Typical changes in the observed vibration characteristics (i.e. change in natural frequencies) are considered as inputs to identify the existence, location and magnitude of delaminations. The performance of the proposed approach is demonstrated using numerical models of composite laminates. Since this identification problem essentially involves the solution of an optimization problem, the use of finite element (FE) methods as the underlying tool for analysis turns out to be computationally expensive. A surrogate assisted optimization approach is hence introduced to contain the computational time within affordable limits. An artificial neural network (ANN) model with Bayesian regularization is used as the underlying approximation scheme while an improved rate of convergence is achieved using a memetic algorithm. However, building of ANN surrogate models usually requires large training datasets. K-means clustering is effectively employed to reduce the size of datasets. ANN is also used via inverse modeling to determine the position, size and location of delaminations using changes in measured natural frequencies. The results clearly highlight the efficiency and the robustness of the approach.

Evolutionary computing based approach for the removal of ECG artifact from the corrupted EEG signal.

PubMed

Priyadharsini, S Suja; Rajan, S Edward

2014-01-01

Electroencephalogram (EEG) is an important tool for clinical diagnosis of brain-related disorders and problems. However, it is corrupted by various biological artifacts, of which ECG is one among them that reduces the clinical importance of EEG especially for epileptic patients and patients with short neck. To remove the ECG artifact from the measured EEG signal using an evolutionary computing approach based on the concept of Hybrid Adaptive Neuro-Fuzzy Inference System, which helps the Neurologists in the diagnosis and follow-up of encephalopathy. The proposed hybrid learning methods are ANFIS-MA and ANFIS-GA, which uses Memetic Algorithm (MA) and Genetic algorithm (GA) for tuning the antecedent and consequent part of the ANFIS structure individually. The performances of the proposed methods are compared with that of ANFIS and adaptive Recursive Least Squares (RLS) filtering algorithm. The proposed methods are experimentally validated by applying it to the simulated data sets, subjected to non-linearity condition and real polysomonograph data sets. Performance metrics such as sensitivity, specificity and accuracy of the proposed method ANFIS-MA, in terms of correction rate are found to be 93.8%, 100% and 99% respectively, which is better than current state-of-the-art approaches. The evaluation process used and demonstrated effectiveness of the proposed method proves that ANFIS-MA is more effective in suppressing ECG artifacts from the corrupted EEG signals than ANFIS-GA, ANFIS and RLS algorithm.

Adaptive surrogate model based multiobjective optimization for coastal aquifer management

NASA Astrophysics Data System (ADS)

Song, Jian; Yang, Yun; Wu, Jianfeng; Wu, Jichun; Sun, Xiaomin; Lin, Jin

2018-06-01

In this study, a novel surrogate model assisted multiobjective memetic algorithm (SMOMA) is developed for optimal pumping strategies of large-scale coastal groundwater problems. The proposed SMOMA integrates an efficient data-driven surrogate model with an improved non-dominated sorted genetic algorithm-II (NSGAII) that employs a local search operator to accelerate its convergence in optimization. The surrogate model based on Kernel Extreme Learning Machine (KELM) is developed and evaluated as an approximate simulator to generate the patterns of regional groundwater flow and salinity levels in coastal aquifers for reducing huge computational burden. The KELM model is adaptively trained during evolutionary search to satisfy desired fidelity level of surrogate so that it inhibits error accumulation of forecasting and results in correctly converging to true Pareto-optimal front. The proposed methodology is then applied to a large-scale coastal aquifer management in Baldwin County, Alabama. Objectives of minimizing the saltwater mass increase and maximizing the total pumping rate in the coastal aquifers are considered. The optimal solutions achieved by the proposed adaptive surrogate model are compared against those solutions obtained from one-shot surrogate model and original simulation model. The adaptive surrogate model does not only improve the prediction accuracy of Pareto-optimal solutions compared with those by the one-shot surrogate model, but also maintains the equivalent quality of Pareto-optimal solutions compared with those by NSGAII coupled with original simulation model, while retaining the advantage of surrogate models in reducing computational burden up to 94% of time-saving. This study shows that the proposed methodology is a computationally efficient and promising tool for multiobjective optimizations of coastal aquifer managements.

Kinetics of Huperzine A Dissociation from Acetylcholinesterase via Multiple Unbinding Pathways.

PubMed

Rydzewski, J; Jakubowski, R; Nowak, W; Grubmüller, H

2018-06-12

The dissociation of huperzine A (hupA) from Torpedo californica acetylcholinesterase ( TcAChE) was investigated by 4 μs unbiased and biased all-atom molecular dynamics (MD) simulations in explicit solvent. We performed our study using memetic sampling (MS) for the determination of reaction pathways (RPs), metadynamics to calculate free energy, and maximum-likelihood estimation (MLE) to recover kinetic rates from unbiased MD simulations. Our simulations suggest that the dissociation of hupA occurs mainly via two RPs: a front door along the axis of the active-site gorge (pwf) and through a new transient side door (pws), i.e., formed by the Ω-loop (residues 67-94 of TcAChE). An analysis of the inhibitor unbinding along the RPs suggests that pws is opened transiently after hupA and the Ω-loop reach a low free-energy transition state characterized by the orientation of the pyridone group of the inhibitor directed toward the Ω-loop plane. Unlike pws, pwf does not require large structural changes in TcAChE to be accessible. The estimated free energies and rates agree well with available experimental data. The dissociation rates along the unbinding pathways are similar, suggesting that the dissociation of hupA along pws is likely to be relevant. This indicates that perturbations to hupA- TcAChE interactions could potentially induce pathway hopping. In summary, our results characterize the slow-onset inhibition of TcAChE by hupA, which may provide the structural and energetic bases for the rational design of the next-generation slow-onset inhibitors with optimized pharmacokinetic properties for the treatment of Alzheimer's disease.

URPD: a specific product primer design tool.

PubMed

Chuang, Li-Yeh; Cheng, Yu-Huei; Yang, Cheng-Hong

2012-06-19

Polymerase chain reaction (PCR) plays an important role in molecular biology. Primer design fundamentally determines its results. Here, we present a currently available software that is not located in analyzing large sequence but used for a rather straight-forward way of visualizing the primer design process for infrequent users. URPD (yoUR Primer Design), a web-based specific product primer design tool, combines the NCBI Reference Sequences (RefSeq), UCSC In-Silico PCR, memetic algorithm (MA) and genetic algorithm (GA) primer design methods to obtain specific primer sets. A friendly user interface is accomplished by built-in parameter settings. The incorporated smooth pipeline operations effectively guide both occasional and advanced users. URPD contains an automated process, which produces feasible primer pairs that satisfy the specific needs of the experimental design with practical PCR amplifications. Visual virtual gel electrophoresis and in silico PCR provide a simulated PCR environment. The comparison of Practical gel electrophoresis comparison to virtual gel electrophoresis facilitates and verifies the PCR experiment. Wet-laboratory validation proved that the system provides feasible primers. URPD is a user-friendly tool that provides specific primer design results. The pipeline design path makes it easy to operate for beginners. URPD also provides a high throughput primer design function. Moreover, the advanced parameter settings assist sophisticated researchers in performing experiential PCR. Several novel functions, such as a nucleotide accession number template sequence input, local and global specificity estimation, primer pair redesign, user-interactive sequence scale selection, and virtual and practical PCR gel electrophoresis discrepancies have been developed and integrated into URPD. The URPD program is implemented in JAVA and freely available at http://bio.kuas.edu.tw/urpd/.

QAPgrid: a two level QAP-based approach for large-scale data analysis and visualization.

PubMed

Inostroza-Ponta, Mario; Berretta, Regina; Moscato, Pablo

2011-01-18

The visualization of large volumes of data is a computationally challenging task that often promises rewarding new insights. There is great potential in the application of new algorithms and models from combinatorial optimisation. Datasets often contain "hidden regularities" and a combined identification and visualization method should reveal these structures and present them in a way that helps analysis. While several methodologies exist, including those that use non-linear optimization algorithms, severe limitations exist even when working with only a few hundred objects. We present a new data visualization approach (QAPgrid) that reveals patterns of similarities and differences in large datasets of objects for which a similarity measure can be computed. Objects are assigned to positions on an underlying square grid in a two-dimensional space. We use the Quadratic Assignment Problem (QAP) as a mathematical model to provide an objective function for assignment of objects to positions on the grid. We employ a Memetic Algorithm (a powerful metaheuristic) to tackle the large instances of this NP-hard combinatorial optimization problem, and we show its performance on the visualization of real data sets. Overall, the results show that QAPgrid algorithm is able to produce a layout that represents the relationships between objects in the data set. Furthermore, it also represents the relationships between clusters that are feed into the algorithm. We apply the QAPgrid on the 84 Indo-European languages instance, producing a near-optimal layout. Next, we produce a layout of 470 world universities with an observed high degree of correlation with the score used by the Academic Ranking of World Universities compiled in the The Shanghai Jiao Tong University Academic Ranking of World Universities without the need of an ad hoc weighting of attributes. Finally, our Gene Ontology-based study on Saccharomyces cerevisiae fully demonstrates the scalability and precision of our method

Hybridisations of Variable Neighbourhood Search and Modified Simplex Elements to Harmony Search and Shuffled Frog Leaping Algorithms for Process Optimisations

NASA Astrophysics Data System (ADS)

Aungkulanon, P.; Luangpaiboon, P.

2010-10-01

Nowadays, the engineering problem systems are large and complicated. An effective finite sequence of instructions for solving these problems can be categorised into optimisation and meta-heuristic algorithms. Though the best decision variable levels from some sets of available alternatives cannot be done, meta-heuristics is an alternative for experience-based techniques that rapidly help in problem solving, learning and discovery in the hope of obtaining a more efficient or more robust procedure. All meta-heuristics provide auxiliary procedures in terms of their own tooled box functions. It has been shown that the effectiveness of all meta-heuristics depends almost exclusively on these auxiliary functions. In fact, the auxiliary procedure from one can be implemented into other meta-heuristics. Well-known meta-heuristics of harmony search (HSA) and shuffled frog-leaping algorithms (SFLA) are compared with their hybridisations. HSA is used to produce a near optimal solution under a consideration of the perfect state of harmony of the improvisation process of musicians. A meta-heuristic of the SFLA, based on a population, is a cooperative search metaphor inspired by natural memetics. It includes elements of local search and global information exchange. This study presents solution procedures via constrained and unconstrained problems with different natures of single and multi peak surfaces including a curved ridge surface. Both meta-heuristics are modified via variable neighbourhood search method (VNSM) philosophy including a modified simplex method (MSM). The basic idea is the change of neighbourhoods during searching for a better solution. The hybridisations proceed by a descent method to a local minimum exploring then, systematically or at random, increasingly distant neighbourhoods of this local solution. The results show that the variant of HSA with VNSM and MSM seems to be better in terms of the mean and variance of design points and yields.

URPD: a specific product primer design tool

PubMed Central

2012-01-01

Background Polymerase chain reaction (PCR) plays an important role in molecular biology. Primer design fundamentally determines its results. Here, we present a currently available software that is not located in analyzing large sequence but used for a rather straight-forward way of visualizing the primer design process for infrequent users. Findings URPD (yoUR Primer Design), a web-based specific product primer design tool, combines the NCBI Reference Sequences (RefSeq), UCSC In-Silico PCR, memetic algorithm (MA) and genetic algorithm (GA) primer design methods to obtain specific primer sets. A friendly user interface is accomplished by built-in parameter settings. The incorporated smooth pipeline operations effectively guide both occasional and advanced users. URPD contains an automated process, which produces feasible primer pairs that satisfy the specific needs of the experimental design with practical PCR amplifications. Visual virtual gel electrophoresis and in silico PCR provide a simulated PCR environment. The comparison of Practical gel electrophoresis comparison to virtual gel electrophoresis facilitates and verifies the PCR experiment. Wet-laboratory validation proved that the system provides feasible primers. Conclusions URPD is a user-friendly tool that provides specific primer design results. The pipeline design path makes it easy to operate for beginners. URPD also provides a high throughput primer design function. Moreover, the advanced parameter settings assist sophisticated researchers in performing experiential PCR. Several novel functions, such as a nucleotide accession number template sequence input, local and global specificity estimation, primer pair redesign, user-interactive sequence scale selection, and virtual and practical PCR gel electrophoresis discrepancies have been developed and integrated into URPD. The URPD program is implemented in JAVA and freely available at http://bio.kuas.edu.tw/urpd/. PMID:22713312

Zinc stimulates glucose oxidation and glycemic control by modulating the insulin signaling pathway in human and mouse skeletal muscle cell lines.

PubMed

Norouzi, Shaghayegh; Adulcikas, John; Sohal, Sukhwinder Singh; Myers, Stephen

2018-01-01

Zinc is a metal ion that is an essential cell signaling molecule. Highlighting this, zinc is an insulin mimetic, activating cellular pathways that regulate cellular homeostasis and physiological responses. Previous studies have linked dysfunctional zinc signaling with several disease states including cancer, obesity, cardiovascular disease and type 2 diabetes. The present study evaluated the insulin-like effects of zinc on cell signaling molecules including tyrosine, PRSA40, Akt, ERK1/2, SHP-2, GSK-3β and p38, and glucose oxidation in human and mouse skeletal muscle cells. Insulin and zinc independently led to the phosphorylation of these proteins over a 60-minute time course in both mouse and human skeletal muscle cells. Similarly, utilizing a protein array we identified that zinc could active the phosphorylation of p38, ERK1/2 and GSK-3B in human and ERK1/2 and GSK-3B in mouse skeletal muscle cells. Glucose oxidation assays were performed on skeletal muscle cells treated with insulin, zinc, or a combination of both and resulted in a significant induction of glucose consumption in mouse (p<0.01) and human (p<0.05) skeletal muscle cells when treated with zinc alone. Insulin, as expected, increased glucose oxidation in mouse (p<0.001) and human (0.001) skeletal muscle cells, however the combination of zinc and insulin did not augment glucose consumption in these cells. Zinc acts as an insulin mimetic, activating key molecules implicated in cell signaling to maintain glucose homeostasis in mouse and human skeletal muscle cells. Zinc is an important metal ion implicated in several biological processes. The role of zinc as an insulin memetic in activating key signaling molecules involved in glucose homeostasis could provide opportunities to utilize this ion therapeutically in treating disorders associated with dysfunctional zinc signaling.

Political Institutions and Their Historical Dynamics

PubMed Central

Sandberg, Mikael; Lundberg, Per

2012-01-01

Traditionally, political scientists define political institutions deductively. This approach may prevent from discovery of existing institutions beyond the definitions. Here, a principal component analysis was used for an inductive extraction of dimensions in Polity IV data on the political institutions of all nations in the world the last two centuries. Three dimensions of institutions were revealed: core institutions of democracy, oligarchy, and despotism. We show that, historically and on a world scale, the dominance of the core institutions of despotism has first been replaced by a dominance of the core institutions of oligarchy, which in turn is now being followed by an increasing dominance by the core institutions of democracy. Nations do not take steps from despotic, to oligarchic and then to democratic institutions, however. Rather, nations hosting the core democracy institutions have succeeded in historically avoiding both the core institutions of despotism and those of oligarchy. On the other hand, some nations have not been influenced by any of these dimensions, while new institutional combinations are increasingly influencing others. We show that the extracted institutional dimensions do not correspond to the Polity scores for autocracy, “anocracy” and democracy, suggesting that changes in regime types occur at one level, while institutional dynamics work on another. Political regime types in that sense seem “canalized”, i.e., underlying institutional architectures can and do vary, but to a considerable extent independently of regime types and their transitions. The inductive approach adds to the deductive regime type studies in that it produces results in line with modern studies of cultural evolution and memetic institutionalism in which institutions are the units of observation, not the nations that acts as host for them. PMID:23056219

Application of Shuffled Frog Leaping Algorithm and Genetic Algorithm for the Optimization of Urban Stormwater Drainage

NASA Astrophysics Data System (ADS)

Kumar, S.; Kaushal, D. R.; Gosain, A. K.

2017-12-01

Urban hydrology will have an increasing role to play in the sustainability of human settlements. Expansion of urban areas brings significant changes in physical characteristics of landuse. Problems with administration of urban flooding have their roots in concentration of population within a relatively small area. As watersheds are urbanized, infiltration decreases, pattern of surface runoff is changed generating high peak flows, large runoff volumes from urban areas. Conceptual rainfall-runoff models have become a foremost tool for predicting surface runoff and flood forecasting. Manual calibration is often time consuming and tedious because of the involved subjectivity, which makes automatic approach more preferable. The calibration of parameters usually includes numerous criteria for evaluating the performances with respect to the observed data. Moreover, derivation of objective function assosciat6ed with the calibration of model parameters is quite challenging. Various studies dealing with optimization methods has steered the embracement of evolution based optimization algorithms. In this paper, a systematic comparison of two evolutionary approaches to multi-objective optimization namely shuffled frog leaping algorithm (SFLA) and genetic algorithms (GA) is done. SFLA is a cooperative search metaphor, stimulated by natural memetics based on the population while, GA is based on principle of survival of the fittest and natural evolution. SFLA and GA has been employed for optimizing the major parameters i.e. width, imperviousness, Manning's coefficient and depression storage for the highly urbanized catchment of Delhi, India. The study summarizes the auto-tuning of a widely used storm water management model (SWMM), by internal coupling of SWMM with SFLA and GA separately. The values of statistical parameters such as, Nash-Sutcliffe efficiency (NSE) and Percent Bias (PBIAS) were found to lie within the acceptable limit, indicating reasonably good model performance

QAPgrid: A Two Level QAP-Based Approach for Large-Scale Data Analysis and Visualization

PubMed Central

Inostroza-Ponta, Mario; Berretta, Regina; Moscato, Pablo

2011-01-01

Background The visualization of large volumes of data is a computationally challenging task that often promises rewarding new insights. There is great potential in the application of new algorithms and models from combinatorial optimisation. Datasets often contain “hidden regularities” and a combined identification and visualization method should reveal these structures and present them in a way that helps analysis. While several methodologies exist, including those that use non-linear optimization algorithms, severe limitations exist even when working with only a few hundred objects. Methodology/Principal Findings We present a new data visualization approach (QAPgrid) that reveals patterns of similarities and differences in large datasets of objects for which a similarity measure can be computed. Objects are assigned to positions on an underlying square grid in a two-dimensional space. We use the Quadratic Assignment Problem (QAP) as a mathematical model to provide an objective function for assignment of objects to positions on the grid. We employ a Memetic Algorithm (a powerful metaheuristic) to tackle the large instances of this NP-hard combinatorial optimization problem, and we show its performance on the visualization of real data sets. Conclusions/Significance Overall, the results show that QAPgrid algorithm is able to produce a layout that represents the relationships between objects in the data set. Furthermore, it also represents the relationships between clusters that are feed into the algorithm. We apply the QAPgrid on the 84 Indo-European languages instance, producing a near-optimal layout. Next, we produce a layout of 470 world universities with an observed high degree of correlation with the score used by the Academic Ranking of World Universities compiled in the The Shanghai Jiao Tong University Academic Ranking of World Universities without the need of an ad hoc weighting of attributes. Finally, our Gene Ontology-based study on Saccharomyces

The evolutionary nature of narratives about expansion and sustenance

NASA Astrophysics Data System (ADS)

Raupach, M. R.

2014-12-01

The 200 years since the start of the industrial era has been a period of rapid and almost unbroken economic growth in much of the world, based upon exponentially increasing use of energy and water resources and the atmospheric commons. It is axiomatic that exponential growth cannot continue forever on a finite planet, leading to an emerging collision between the presently irresistible force of economic growth and the immovable reality of the finitude of Planet Earth. This has led to the a contest between two broad narratives about humans and their planet in the 21st century, an "expansion" narrative framed around the paramount need for economic growth, and a "sustenance" narrative framed around the paramount need to protect an increasingly fragile natural world. Many features of recent public discourse, including the acceleration of the news cycle and the echo-chamber effect of interactive social media, have driven these narratives to become progressively more mutually antagonistic and incompatible. Here I explore the idea that narratives (in the sense of stories that empower actions) are meme sequences that evolve through diversification, selection and adaptation. This memetic evolution can be understood and, to some extent, influenced. An analogy might be with the influence exerted by human selection over centuries on the gene pool of domesticated animals and plants. In shaping our shared future, the evolutionary contest between "expansion" and "sustenance" narratives is just as important as the dynamics of the natural world. The future therefore depends upon the evolution of more subtle and resilient narratives about human-earth interactions. A selection test for these narratives is their ability to empower a transition to a society that lives within the means of a finite planet and improves global wellbeing at the same time. My own recent experience is that scientists alone are not very good at shaping narratives to pass this fitness test, and the participation

A population-based evolutionary search approach to the multiple minima problem in de novo protein structure prediction

PubMed Central

2013-01-01

Background Elucidating the native structure of a protein molecule from its sequence of amino acids, a problem known as de novo structure prediction, is a long standing challenge in computational structural biology. Difficulties in silico arise due to the high dimensionality of the protein conformational space and the ruggedness of the associated energy surface. The issue of multiple minima is a particularly troublesome hallmark of energy surfaces probed with current energy functions. In contrast to the true energy surface, these surfaces are weakly-funneled and rich in comparably deep minima populated by non-native structures. For this reason, many algorithms seek to be inclusive and obtain a broad view of the low-energy regions through an ensemble of low-energy (decoy) conformations. Conformational diversity in this ensemble is key to increasing the likelihood that the native structure has been captured. Methods We propose an evolutionary search approach to address the multiple-minima problem in decoy sampling for de novo structure prediction. Two population-based evolutionary search algorithms are presented that follow the basic approach of treating conformations as individuals in an evolving population. Coarse graining and molecular fragment replacement are used to efficiently obtain protein-like child conformations from parents. Potential energy is used both to bias parent selection and determine which subset of parents and children will be retained in the evolving population. The effect on the decoy ensemble of sampling minima directly is measured by additionally mapping a conformation to its nearest local minimum before considering it for retainment. The resulting memetic algorithm thus evolves not just a population of conformations but a population of local minima. Results and conclusions Results show that both algorithms are effective in terms of sampling conformations in proximity of the known native structure. The additional minimization is shown to be

Methyl-methionine as a precursor for methyl chloride and dimethyl sulphide produced in terrestrial salt lakes

NASA Astrophysics Data System (ADS)

Mulder, I.; Krause, T.; Studenroth, S.; Tubbesing, C.; Kotte, K.; Schöler, H. F.

2012-04-01

the MeCl formation from methyl-methionine (Me-MET) and, in addition, structurally related compounds to methionine in order to understand the formation mechanism of MeCl and DMS. Our results showed that an emission of MeCl and DMS from salt pans via MET/Me-MET decomposition appears plausible. Harnisch and Eisenhauer, Geophys. Research Letters, 1998, 25, No.13, 2401-2404 Keppler et al., Atmos. Chem. Phys., 2005, 5, 2403 Kloster et al., Biogeosciences, 2006, 3, 29-51 Montzka et al., Chapter 1, Scientific Assessment of Ozone Depletion: 2002, Global Ozone Research and Monitoring Project (47), 2003, 1.1-1.83 Svensen et al., Earth and Planetary Science Letters 277, 2009, 490-500 Sievert et al., Oceanography, 2007, 20 , No.2

PREFACE: Annual Conference on Functional Materials and Nanotechnologies - FM&NT 2011

NASA Astrophysics Data System (ADS)

Sternberg, Andris; Muzikante, Inta; Zicans, Janis

2011-06-01

Conference photograph ERAF logo International Organizing Committee Andris Sternberg (chairperson), Institute of Solid State Physics, University of Latvia, Latvia, MATERA Juras Banys, Vilnius University, Lithuania Gunnar Borstel, University of Osnabrück, Germany Niels E Christensen, University of Aarhus, Denmark Robert A Evarestov, St. Petersburg State University, Russia Claes-Goran Granqvist, Uppsala University, Sweden Dag Høvik, The Research Council of Norway, Norway, MATERA Marco Kirm, Institute of Physics, University of Tartu, Estonia Vladislav Lemanov, Ioffe Physical Technical Institute, Russia Witold Lojkowski, Institute of High Pressure Physics, Poland Ergo Nommiste, University of Tartu, Estonia Helmut Schober, Institut Laue-Langevin, France Sisko Sipilä, Finnish Funding Agency for Technology and Innovation, Finland, MATERA Ingólfur Torbjörnsson, Icelandic Centre for Research, Iceland, MATERA Marcel H Van de Voorde, University of Technology Delft, The Netherlands International Program Committee Inta Muzikante (chairperson), Institute of Solid State Physics, University of Latvia, Latvia, MATERA Liga Berzina-Cimdina, Institute of Biomaterials and Biomechanics, Riga Technical University, Latvia Janis Grabis, Institute of Inorganic Chemistry, Riga Technical University, Latvia Leonid V Maksimov, Vavilov State Optical Institute, Russia Linards Skuja, Institute of Solid State Physics, University of Latvia, Latvia Maris Springis, Institute of Solid State Physics, University of Latvia, Latvia Ilmars Zalite, Institute of Inorganic Chemistry, Riga Technical University, Latvia Janis Zicans, Institute of Polymers, Riga Technical University Local Committee: Liga Grinberga, Anatolijs Sarakovskis, Jurgis Grube, Raitis Siatkovskis, Maris Kundzins, Anna Muratova, Maris Springis, Aivars Vembris, Krisjanis Smits, Andris Fedotovs, Dmitrijs Bocarovs, Anastasija Jozepa, Andris Krumins.

Life, Information, Entropy, and Time: Vehicles for Semantic Inheritance.

PubMed

Crofts, Antony R

2007-01-01

evolution. The initial interpretational steps include protein synthesis, molecular recognition, and catalytic potential that facilitate structural and functional roles. Combinatorial possibilities are extended through interactions of increasing complexity in the temporal dimension. (3) All living things show a behavior that indicates awareness of time, or chronognosis. The ∼4 billion years of biological evolution have given rise to forms with increasing sophistication in sensory adaptation. This has been linked to the development of an increasing chronognostic range, and an associated increase in combinatorial complexity. (4) Development of a modern human phenotype and the ability to communicate through language, led to the development of archival storage, and invention of the basic skills, institutions and mechanisms that allowed the evolution of modern civilizations. Combinatorial amplification at the supra-phenotypical level arose from the invention of syntax, grammar, numbers, and the subsequent developments of abstraction in writing, algorithms, etc. The translational machineries of the human mind, the "mutation" of ideas therein, and the "conversations" of our social intercourse, have allowed a limited set of symbolic descriptors to evolve into an exponentially expanding semantic heritage. (5) The three postulates above open interesting epistemological questions. An understanding of topics such dualism, the élan vital, the status of hypothesis in science, memetics, the nature of consciousness, the role of semantic processing in the survival of societies, and Popper's three worlds, require recognition of an insubstantial component. By recognizing a necessary linkage between semantic content and a physical machinery, we can bring these perennial problems into the framework of a realistic philosophy. It is suggested, following Popper, that the ∼4 billion years of evolution of the biosphere represents an exploration of the nature of reality at the physicochemical

Using hydrological modelling for a preliminary assessment of under-catch of precipitation in some Alpine Catchments of Sierra Nevada (Spain). Sensitivity to different conceptual approaches and spatio-temporal scale

NASA Astrophysics Data System (ADS)

Jimeno-Saez, Patricia; Pulido-Velazquez, David; Pegalajar-Cuellar, Manuel; Collados-Lara, Antonio-Juan; Pardo-Iguzquiza, Eulogio

2017-04-01

snow are considered. Correction factors of the solid & liquid P fields have been included in the formulation. We intend to perform an automatic calibration of the parameters of these models. A detailed analysis of global optimization techniques has been performed in order to identify the best possible optimization algorithm (Classic Informed Local Search, Simulated Annealing, Genetic Algorithm and Memetic algorithm) which is important due to the high computational cost of our optimization problems with many parameters and noisy inputs and outputs. Finally with the best calibration algorithm we have performed different optimization experiments (20 realizations). It allows us to obtain a distribution function of the correction factor for the solid and liquid P for each catchment, which can be useful as a preliminary assessment of the global under-catch in the basins. We have also analysed the sensitivity of the results to the spatio-temporal scale (grid with cells of 1x1 kms or 12.5x12.5 Kms; daily or monthly approaches) employed to approach different hydrological processes. We are also working in the analysis of these issues considering multi-objective evolutionary optimization approaches for calibration using multiple target criteria in which the transient calibration try to minimize differences with both, stream flow and snow cover area observations. This research has been partially supported by the CGL2013-48424-C2-2-R (MINECO) and the PMAFI/06/14 (UCAM) projects.

Life, Information, Entropy, and Time

PubMed Central

Crofts, Antony R.

2008-01-01

by evolution. The initial interpretational steps include protein synthesis, molecular recognition, and catalytic potential that facilitate structural and functional roles. Combinatorial possibilities are extended through interactions of increasing complexity in the temporal dimension. (3) All living things show a behavior that indicates awareness of time, or chronognosis. The ∼4 billion years of biological evolution have given rise to forms with increasing sophistication in sensory adaptation. This has been linked to the development of an increasing chronognostic range, and an associated increase in combinatorial complexity. (4) Development of a modern human phenotype and the ability to communicate through language, led to the development of archival storage, and invention of the basic skills, institutions and mechanisms that allowed the evolution of modern civilizations. Combinatorial amplification at the supra-phenotypical level arose from the invention of syntax, grammar, numbers, and the subsequent developments of abstraction in writing, algorithms, etc. The translational machineries of the human mind, the “mutation” of ideas therein, and the “conversations” of our social intercourse, have allowed a limited set of symbolic descriptors to evolve into an exponentially expanding semantic heritage. (5) The three postulates above open interesting epistemological questions. An understanding of topics such dualism, the élan vital, the status of hypothesis in science, memetics, the nature of consciousness, the role of semantic processing in the survival of societies, and Popper's three worlds, require recognition of an insubstantial component. By recognizing a necessary linkage between semantic content and a physical machinery, we can bring these perennial problems into the framework of a realistic philosophy. It is suggested, following Popper, that the ∼4 billion years of evolution of the biosphere represents an exploration of the nature of reality at the

PREFACE: International Conference on Functional Materials and Nanotechnologies (FM&NT2012)

NASA Astrophysics Data System (ADS)

Sternberg, Andris; Muzikante, Inta; Sarakovskis, Anatolijs; Grinberga, Liga

2012-08-01

, Institute of Solid State Physics, University of Latvia, Latvia 8. Maris Springis, Institute of Solid State Physics, University of Latvia, Latvia 9. Ilmars Zalite, Institute of Inorganic Chemistry, Riga Technical University, Latvia 10. Janis Zicans, Institute of Polymers, Riga Technical University, Latvia Local Committee Liga Grinberga, Anatolijs Sarakovskis, Jurgis Grube, Maris Kundzins, Anastasija Jozepa, Anna Muratova, Raitis Siatkovskis, Andris Fedotovs, Dmitrijs Bocarovs, Sniedze Abele, Mikus Voss, Andris Sivars, Peteris Lesnicenoks, Virginija Liepina. In Memoriam Dr. habil. phys. Inta Muzikante (08.01.1951-15.02.2012) Inta Muzikante Inta was born in Valmiera, a town in the northern part of Latvia. She attended school in Sigulda and high school in Riga. While at the high-school, Inta decided to study natural sciences. After graduating from high-school in 1969 she entered the physics section of the Physics and Mathematics department of University of Latvia and obtained her university degree in 1974. In parallel with University studies, Inta started to work at the Semiconductor Physics Research Lab at the University of Latvia. After graduating she was offered a position at the Physical Energetics institute of the Latvian Academy of Sciences, in the laboratory of Professor Edgars Silinsh, one of the most internationally well known Latvian physicists. Inta started researching electronic and photoelectric processes in organic crystals and thin films. This was a novel field, pioneered both internationally and in Latvia by Profesors E Silinsh, O Neilands and J Freimanis. It could be said that Inta stood at the cradle of this research field and stayed faithful to it all of her life. Her work was very successful and within a few years she advanced from research assistant to researcher and then leading research scientist. Her first scientific topic was studies of the mechanism of charge carrier photogeneration and separation in organic molecular crystals. In 1983 for a work