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Publisher: Springer-Verlag   (Total: 2209 journals)

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e & i Elektrotechnik und Informationstechnik     Hybrid Journal   (Followers: 8, SJR: 0.146, h-index: 8)
e-Neuroforum     Hybrid Journal  
Early Childhood Education J.     Hybrid Journal   (Followers: 12, SJR: 0.367, h-index: 12)
Earth Science Informatics     Hybrid Journal   (Followers: 3, SJR: 0.245, h-index: 5)
Earth, Moon, and Planets     Hybrid Journal   (Followers: 5, SJR: 0.436, h-index: 28)
Earthquake Engineering and Engineering Vibration     Hybrid Journal   (Followers: 7, SJR: 0.433, h-index: 17)
Earthquake Science     Hybrid Journal   (Followers: 9, SJR: 0.486, h-index: 7)
East Asia     Hybrid Journal   (Followers: 7, SJR: 0.165, h-index: 9)
Eating and Weight Disorders - Studies on Anorexia, Bulimia and Obesity     Hybrid Journal   (Followers: 7, SJR: 0.289, h-index: 23)
EcoHealth     Hybrid Journal   (Followers: 1, SJR: 0.651, h-index: 22)
Ecological Research     Hybrid Journal   (Followers: 7, SJR: 0.698, h-index: 38)
Economic Botany     Hybrid Journal   (Followers: 8, SJR: 0.666, h-index: 40)
Economic Bulletin     Hybrid Journal   (Followers: 4)
Economic Change and Restructuring     Hybrid Journal   (Followers: 1, SJR: 0.263, h-index: 6)
Economic Theory     Hybrid Journal   (Followers: 6, SJR: 1.857, h-index: 31)
Economic Theory Bulletin     Hybrid Journal   (Followers: 1)
Economics of Governance     Hybrid Journal   (Followers: 2, SJR: 0.367, h-index: 12)
Ecosystems     Hybrid Journal   (Followers: 19, SJR: 1.793, h-index: 83)
Ecotoxicology     Hybrid Journal   (Followers: 10, SJR: 1.041, h-index: 53)
Education and Information Technologies     Hybrid Journal   (Followers: 156, SJR: 0.207, h-index: 15)
Educational Assessment, Evaluation and Accountability     Hybrid Journal   (Followers: 13, SJR: 0.519, h-index: 14)
Educational Psychology Review     Hybrid Journal   (Followers: 14, SJR: 1.781, h-index: 52)
Educational Research for Policy and Practice     Hybrid Journal   (Followers: 6, SJR: 0.211, h-index: 8)
Educational Studies in Mathematics     Hybrid Journal   (Followers: 9, SJR: 0.946, h-index: 27)
Educational Technology Research and Development     Partially Free   (Followers: 171, SJR: 1.124, h-index: 45)
Electrical Engineering     Hybrid Journal   (Followers: 11, SJR: 0.352, h-index: 17)
Electrocatalysis     Hybrid Journal   (SJR: 0.542, h-index: 7)
Electronic Commerce Research     Hybrid Journal   (Followers: 3, SJR: 0.636, h-index: 14)
Electronic Markets     Hybrid Journal   (Followers: 5, SJR: 0.326, h-index: 5)
Electronic Materials Letters     Hybrid Journal   (Followers: 3, SJR: 0.566, h-index: 11)
Elemente der Mathematik     Hybrid Journal  
Emergency Radiology     Hybrid Journal   (Followers: 4, SJR: 0.446, h-index: 22)
Empirica     Hybrid Journal   (Followers: 3, SJR: 0.185, h-index: 12)
Empirical Economics     Hybrid Journal   (Followers: 8, SJR: 0.5, h-index: 29)
Empirical Software Engineering     Hybrid Journal   (Followers: 4, SJR: 2.319, h-index: 33)
Employee Responsibilities and Rights J.     Hybrid Journal   (Followers: 2, SJR: 0.21, h-index: 13)
Endocrine     Hybrid Journal   (Followers: 4, SJR: 0.659, h-index: 55)
Endocrine Pathology     Hybrid Journal   (Followers: 2, SJR: 0.555, h-index: 27)
Energy Efficiency     Hybrid Journal   (Followers: 11, SJR: 1.056, h-index: 10)
Energy Systems     Hybrid Journal   (Followers: 9, SJR: 0.589, h-index: 5)
Engineering With Computers     Hybrid Journal   (Followers: 5, SJR: 0.497, h-index: 26)
Entomological Review     Hybrid Journal   (Followers: 3, SJR: 0.128, h-index: 5)
Environment Systems & Decisions     Hybrid Journal   (Followers: 2)
Environment, Development and Sustainability     Hybrid Journal   (Followers: 28, SJR: 0.319, h-index: 26)
Environmental and Ecological Statistics     Hybrid Journal   (Followers: 5, SJR: 0.389, h-index: 29)
Environmental and Resource Economics     Hybrid Journal   (Followers: 17, SJR: 1.651, h-index: 46)
Environmental Biology of Fishes     Hybrid Journal   (Followers: 3, SJR: 0.486, h-index: 53)
Environmental Chemistry Letters     Hybrid Journal   (Followers: 3, SJR: 0.664, h-index: 22)
Environmental Earth Sciences     Hybrid Journal   (Followers: 10, SJR: 0.601, h-index: 55)
Environmental Economics and Policy Studies     Hybrid Journal   (Followers: 6, SJR: 0.35, h-index: 3)
Environmental Evidence     Open Access  
Environmental Fluid Mechanics     Hybrid Journal   (Followers: 2, SJR: 0.732, h-index: 23)
Environmental Geochemistry and Health     Hybrid Journal   (Followers: 2, SJR: 0.909, h-index: 32)
Environmental Geology     Hybrid Journal   (Followers: 11)
Environmental Health and Preventive Medicine     Hybrid Journal   (Followers: 2, SJR: 0.388, h-index: 14)
Environmental Management     Hybrid Journal   (Followers: 31, SJR: 0.773, h-index: 60)
Environmental Modeling & Assessment     Hybrid Journal   (Followers: 11, SJR: 0.413, h-index: 27)
Environmental Monitoring and Assessment     Hybrid Journal   (Followers: 9, SJR: 0.671, h-index: 46)
Environmental Science and Pollution Research     Hybrid Journal   (Followers: 11, SJR: 0.878, h-index: 42)
Epidemiologic Perspectives & Innovations     Open Access   (Followers: 1, SJR: 1.002, h-index: 14)
Epileptic Disorders     Hybrid Journal   (Followers: 1, SJR: 0.669, h-index: 34)
EPJ A - Hadrons and Nuclei     Hybrid Journal   (Followers: 1, SJR: 1.435, h-index: 58)
EPJ B - Condensed Matter and Complex Systems     Hybrid Journal   (Followers: 3, SJR: 0.749, h-index: 85)
EPJ direct     Hybrid Journal  
EPJ E - Soft Matter and Biological Physics     Hybrid Journal   (Followers: 1, SJR: 0.661, h-index: 57)
EPMA J.     Open Access   (SJR: 0.161, h-index: 4)
ERA-Forum     Hybrid Journal   (Followers: 2, SJR: 0.13, h-index: 2)
Erkenntnis     Hybrid Journal   (Followers: 11, SJR: 0.62, h-index: 14)
Erwerbs-Obstbau     Hybrid Journal   (SJR: 0.173, h-index: 8)
Esophagus     Hybrid Journal   (SJR: 0.268, h-index: 9)
Estuaries and Coasts     Hybrid Journal   (Followers: 3, SJR: 1.111, h-index: 61)
Ethical Theory and Moral Practice     Hybrid Journal   (Followers: 7, SJR: 0.278, h-index: 8)
Ethics and Information Technology     Hybrid Journal   (Followers: 177, SJR: 0.363, h-index: 20)
Ethik in der Medizin     Hybrid Journal   (SJR: 0.204, h-index: 6)
Euphytica     Hybrid Journal   (Followers: 7, SJR: 0.709, h-index: 57)
Eurasian Soil Science     Hybrid Journal   (Followers: 2, SJR: 0.271, h-index: 10)
EURO J. of Transportation and Logistics     Hybrid Journal   (Followers: 4)
EURO J. on Computational Optimization     Hybrid Journal  
EURO J. on Decision Processes     Hybrid Journal  
Europaisches J. fur Minderheitenfragen     Hybrid Journal  
European Actuarial J.     Hybrid Journal   (Followers: 3)
European Archives of Oto-Rhino-Laryngology     Hybrid Journal   (Followers: 4, SJR: 0.737, h-index: 37)
European Archives of Paediatric Dentistry     Hybrid Journal   (Followers: 1, SJR: 0.446, h-index: 12)
European Archives of Psychiatry and Clinical Neuroscience     Hybrid Journal   (Followers: 2, SJR: 1.334, h-index: 62)
European Biophysics J.     Hybrid Journal   (Followers: 4, SJR: 0.979, h-index: 53)
European Child & Adolescent Psychiatry     Hybrid Journal   (Followers: 4, SJR: 1.269, h-index: 51)
European Clinics in Obstetrics and Gynaecology     Hybrid Journal   (Followers: 4)
European Food Research and Technology     Hybrid Journal   (Followers: 8, SJR: 0.773, h-index: 49)
European J. for Education Law and Policy     Hybrid Journal   (Followers: 5)
European J. for Philosophy of Science     Partially Free   (Followers: 4, SJR: 0.165, h-index: 2)
European J. of Ageing     Hybrid Journal   (Followers: 8, SJR: 0.49, h-index: 17)
European J. of Applied Physiology     Hybrid Journal   (Followers: 7, SJR: 1.044, h-index: 74)
European J. of Clinical Microbiology & Infectious Diseases     Hybrid Journal   (Followers: 10, SJR: 0.958, h-index: 74)
European J. of Clinical Pharmacology     Hybrid Journal   (Followers: 9, SJR: 0.916, h-index: 69)
European J. of Dermatology     Hybrid Journal   (Followers: 7)
European J. of Drug Metabolism and Pharmacokinetics     Hybrid Journal   (Followers: 6, SJR: 0.24, h-index: 25)
European J. of Epidemiology     Hybrid Journal   (Followers: 17, SJR: 1.946, h-index: 60)
European J. of Forest Research     Hybrid Journal   (Followers: 3, SJR: 0.864, h-index: 25)
European J. of Health Economics     Hybrid Journal   (Followers: 11, SJR: 0.67, h-index: 25)
European J. of Law and Economics     Hybrid Journal   (Followers: 174, SJR: 0.242, h-index: 13)

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Journal Cover Evolutionary Intelligence
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   Hybrid Journal Hybrid journal (It can contain Open Access articles)
     ISSN (Print) 1864-5917 - ISSN (Online) 1864-5909
     Published by Springer-Verlag Homepage  [2209 journals]   [SJR: 0.42]   [H-I: 9]
  • Advancing genetic algorithm approaches to field programmable gate array
           placement with enhanced recombination operators
    • Abstract: Abstract Since their inception, field programmable gate arrays have seen an enormous growth in usage because they can dramatically reduce design and manufacturing costs. However, the time required for placement (a key step in the design) is dominating the compilation process. In this paper, we take some initial theoretical steps towards developing an efficient genetic algorithm for solving the placement problem by developing suitable recombination operators for performing placement. According to Holland, when the genetic algorithm recombines two parent genotypes, the differences between them define a genotypic subspace, and any offspring produced should be confined to this subspace. Those recombination operators that violate this principle can direct a search away from the region containing the parent genotypes and this is contrary to the intended task for recombination. This is often detrimental to search performance. This paper contributes the development of an intuitive visualization technique that can be used to easily detect violations of the previous principle. The efficacy of the proposed methodology is demonstrated and it is demonstrated that many standard recombination operators violate this principle. The methodology is then used to guide the development of novel operators that exhibit substantial (and statistically significant) improvements in performance over standard recombination operators.
      PubDate: 2014-10-02
       
  • The distributed co-evolution of an on-board simulator and controller for
           swarm robot behaviours
    • Abstract: Abstract We investigate the reality gap, specifically the environmental correspondence of an on-board simulator. We describe a novel distributed co-evolutionary approach to improve the transference of controllers that co-evolve with an on-board simulator. A novelty of our approach is the the potential to improve transference between simulation and reality without an explicit measurement between the two domains. We hypothesise that a variation of on-board simulator environment models across many robots can be competitively exploited by comparison of the real controller fitness of many robots. We hypothesise that the real controller fitness values across many robots can be taken as indicative of the varied fitness in environmental correspondence of on-board simulators, and used to inform the distributed evolution an on-board simulator environment model without explicit measurement of the real environment. Our results demonstrate that our approach creates an adaptive relationship between the on-board simulator environment model, the real world behaviour of the robots, and the state of the real environment. The results indicate that our approach is sensitive to whether the real behavioural performance of the robot is informative on the state real environment.
      PubDate: 2014-08-05
       
  • Evolutionary robotics
    • PubDate: 2014-08-01
       
  • An evolutionary robotics approach for the distributed control of satellite
           formations
    • Abstract: Abstract We propose and study a decentralized formation flying control architecture based on the evolutionary robotic technique. We develop our control architecture for the MIT SPHERES robotic platform on board the International Space Station and we show how it is able to achieve micrometre and microradians precision at the path planning level. Our controllers are homogeneous across satellites and do not make use of labels (i.e. all satellites can be exchanged at any time). The evolutionary process is able to produce homogeneous controllers able to plan, with high precision, for the acquisition and maintenance of any triangular formation.
      PubDate: 2014-07-12
       
  • Beyond black-box optimization: a review of selective pressures for
           evolutionary robotics
    • Abstract: Abstract Evolutionary robotics (ER) is often viewed as the application of a family of black-box optimization algorithms—evolutionary algorithms—to the design of robots, or parts of robots. When considering ER as black-box optimization, the selective pressure is mainly driven by a user-defined, black-box fitness function, and a domain-independent selection procedure. However, most ER experiments face similar challenges in similar setups: the selective pressure, and, in particular, the fitness function, is not a pure user-defined black box. The present review shows that, because ER experiments share common features, selective pressures for ER are a subject of research on their own. The literature has been split into two categories: goal refiners, aimed at changing the definition of a good solution, and process helpers, designed to help the search process. Two sub-categories are further considered: task-specific approaches, which require knowledge on how to solve the task and task-agnostic ones, which do not need it. Besides highlighting the diversity of the approaches and their respective goals, the present review shows that many task-agnostic process helpers have been proposed during the last years, thus bringing us closer to the goal of a fully automated robot behavior design process.
      PubDate: 2014-07-03
       
  • Simultaneous versus incremental learning of multiple skills by modular
           robots
    • Abstract: Abstract This paper is concerned with the problem of learning multiple skills by modular robots. The main question we address is whether it is better to learn multiple skills simultaneously (all-at-once) or incrementally (one-by-one). We conduct an experimental study with modular robots of various morphologies that need to acquire three different but correlated skills, efficient locomotion, navigation towards a target point, and obstacle avoidance, using a real-time, on-board evolution as the learning method. The results indicate that the one-by-one strategy is more efficient and more stable than the all-at-once strategy.
      PubDate: 2014-06-28
       
  • Foreword
    • PubDate: 2014-04-01
       
  • Evolution-in-materio: evolving computation in materials
    • Abstract: Abstract Evolution-in-materio (EIM) is the manipulation of a physical system by computer controlled evolution (CCE). It takes the position that to obtain useful functions from a physical system one needs to apply highly specific physical signals and place the system in a particular physical state. It argues that CCE is an effective methodology for doing this. One of the potential advantages of this is that artificial evolution can potentially exploit physical effects that are either too complex to understand or hitherto unknown. EIM is most commonly used as a methodology for implementing computation in physical systems. The method is a hybrid of analogue and classical computation in that it uses classical computers to program physical systems or analogue devices. Thus far EIM has only been attempted in a rather limited set of physical and chemical systems. This review paper examines past work related to EIM and discusses historical underpinnings behind such work. It describes latest developments, gives an analysis of the advantages and disadvantages of such work and the challenges that still remain.
      PubDate: 2014-04-01
       
  • Introduction to the special issue on evolutionary intelligence in games
    • PubDate: 2014-01-24
       
  • Creating autonomous agents for playing Super Mario Bros game by means of
           evolutionary finite state machines
    • Abstract: Abstract This paper shows the design and improvement of an autonomous agent based in using evolutionary methods to improve behavioural models (finite state machines), which are part of the individuals to evolve. This leads to the obtention of a so-called bot that follows the Gameplay track rules of the international Mario AI Championship and is able to autonomously complete different scenarios on a simulator of Super Mario Bros. game. Mono- and multi-seed approaches (evaluation in one play or multiple plays respectively) have been analysed, in order to compare respectively the performance of an approach focused in solving a specific scenario, and another more general, devoted to obtain an agent which can play successfully in different scenarios. The analysis considers the machine resources consumption, which turns in a bottleneck in some experiments. However, the methods yield agents which can finish several stages of different difficulty levels, and playing much better than an expert human player, since they can deal with very difficult situations (several enemies surrounding Mario, for instance) in real time. According to the results and considering the competition’s restrictions (time limitations) and objectives (complete scenarios up to difficulty level 3), these agents have enough performance to participate in this competition track.
      PubDate: 2014-01-24
       
  • On evolutionary subspace clustering with symbiosis
    • Abstract: Abstract Subspace clustering identifies the attribute support for each cluster as well as identifying the location and number of clusters. In the most general case, attributes associated with each cluster could be unique. A multi-objective evolutionary method is proposed to identify the unique attribute support of each cluster while detecting its data instances. The proposed algorithm, symbiotic evolutionary subspace clustering (S-ESC) borrows from ‘symbiosis’ in the sense that each clustering solution is defined in terms of a host (single member of the host population) and a number of coevolved cluster centroids (or symbionts in an independent symbiont population). Symbionts define clusters and therefore attribute subspaces, whereas hosts define sets of clusters to constitute a non-degenerate solution. The symbiotic representation of S-ESC is the key to making it scalable to high-dimensional datasets, while an integrated subsampling process makes it scalable to tasks with a large number of data items. Benchmarking is performed against a test suite of 59 subspace clustering tasks with four well known comparator algorithms from both the full-dimensional and subspace clustering literature: EM, MINECLUS, PROCLUS, STATPC. Performance of the S-ESC algorithm was found to be robust across a wide cross-section of properties with a common parameterization utilized throughout. This was not the case for the comparator algorithms. Specifically, performance could be sensitive to the particular data distribution or parameter sweeps might be necessary to provide comparable performance. An additional evaluation is performed against a non-symbiotic GA, with S-ESC still returning superior clustering solutions.
      PubDate: 2014-01-12
       
  • A fast anomaly detection system using probabilistic artificial immune
           algorithm capable of learning new attacks
    • Abstract: Abstract In this paper, we propose anomaly based intrusion detection algorithms in computer networks using artificial immune systems, capable of learning new attacks. Unique characteristics and observations specific to computer networks are considered in developing faster algorithms while achieving high performance. Although these characteristics play a key role in the proposed algorithms, we believe they have been neglected in the previous related works. We evaluate the proposed algorithms on a number of well-known intrusion detection datasets, as well as two new real datasets extracted from the data networks for intrusion detection. We analyze the detection performance and learning capabilities of the proposed algorithms, in addition to performance criteria such as false alarm rate, detection rate, and response time. The experimental results demonstrate that the proposed algorithms exhibit fast response time, low false alarm rate, and high detection rate. They can also learn new attack patterns, and identify them the next time they are introduced to the network.
      PubDate: 2013-12-25
       
  • Swarm intelligence based algorithms: a critical analysis
    • Abstract: Abstract Many optimization algorithms have been developed by drawing inspiration from swarm intelligence (SI). These SI-based algorithms can have some advantages over traditional algorithms. In this paper, we carry out a critical analysis of these SI-based algorithms by analyzing their ways to mimic evolutionary operators. We also analyze the ways of achieving exploration and exploitation in algorithms by using mutation, crossover and selection. In addition, we also look at algorithms using dynamic systems, self-organization and Markov chain framework. Finally, we provide some discussions and topics for further research.
      PubDate: 2013-12-17
       
  • Overview of Harmony Search algorithm and its applications in Civil
           Engineering
    • Abstract: Abstract Harmony Search (HS), a meta-heuristic algorithm, conceptualizes a musical process of searching for a perfect state of harmony (optimal solution). It allows a random search without initial values and removes the necessity for information of derivatives. Since the HS algorithm was first developed and published in 2001, it has been applied to various research areas and the world wide attention on it has rapidly increased. In this paper, applications of HS algorithm in Civil Engineering (CE) are to be overviewed. Articles in CE areas including water resources, structural, geotechnical, environmental, and traffic engineering are to be reviewed thoroughly. As a results, variety of application results show that HS can be effectively used as a tool for optimization problems in CE.
      PubDate: 2013-12-12
       
  • New solver and optimal anticipation strategies design based on
           evolutionary computation for the game of MasterMind
    • Abstract: Abstract This paper presents and compares several evolutionary solutions for the well-known MasterMind game, a classic board game invented in the 1970s. First, we propose a novel evolutionary approach (which we call nested hierarchical evolutionary search) to solve the MasterMind game, comparing the obtained results with that of existing algorithms. Second, we show how to design novel game anticipation strategies for the MasterMind game, by applying genetic programming. In this case we compare the performance of the new obtained strategies with that of the classical ones, obtaining advantages in all the cases tested.
      PubDate: 2013-12-11
       
  • Reconstructing biological gene regulatory networks: where optimization
           meets big data
    • Abstract: Abstract The importance of ‘big data’ in biology is increasing as vast quantities of data are being produced from high-throughput experiments. Techniques such as DNA microarrays are providing a genome-wide picture of gene expression levels, allowing us to investigate the structure and interactions of gene networks in biological systems. Inference of gene regulatory network (GRN) is an underdetermined problem suited to Metaheuristic algorithms which can operate on limited information. Thus GRN inference offers a platform for investigations into data intensive sciences and large scale optimization problems. Here we examine the link between data intensive research and optimization problems for the reverse engineering of GRNs. Briefly, we detail the benefit of the data deluge and the study of ALife for modelling GRNs as well as their reconstruction. We discuss how metaheuristics can solve big data problems and the inference of GRNs offer real world problems for both areas of research. We overview some current reconstruction algorithms and investigate some modelling and computational limits of the inference processes and suggest some areas for development. Furthermore we identify links and synergies between optimization and big data, e.g., dynamic, uncertain and large scale optimization problems, and discuss the potential benefit of multi- and many-objective optimization. We stress the importance of data integration techniques in order to maximize the data available, particularly for the case of inferring GRNs from microarray data. Such multi-disciplinary research is vital as biology is rapidly becoming a quantitative, data intensive science.
      PubDate: 2013-11-22
       
  • Special issue on advances in Learning Classifier Systems
    • PubDate: 2013-11-08
       
  • Methods for approximating value functions for the Dominion card game
    • Abstract: Abstract Artificial neural networks have been successfully used to approximate value functions for tasks involving decision making. In domains where decisions require a shift in judgment as the overall state changes, it is hypothesized here that methods utilizing multiple artificial neural networks are likely to provide a benefit as an approximation of a value function over those that employ a single network. The card game Dominion was chosen as the domain to examine this. This paper compares artificial neural networks generated by multiple machine learning methods successfully applied to other games (such as in TD-Gammon) to a genetic algorithm method for generating two neural networks for different phases of the game along with evolving the transition point. The results demonstrate a greater success ratio with the genetic algorithm applied to two neural networks. This suggests that future work examining more complex neural network configurations and richer evolutionary exploration could apply to Dominion as well as other domains necessitating shifts in strategy.
      PubDate: 2013-10-24
       
  • A comparative study: the effect of the perturbation vector type in the
           differential evolution algorithm on the accuracy of robot pose and heading
           estimation
    • Abstract: Abstract Evolutionary algorithms (EAs) belong to a group of classic optimizers these days, and can be used in many application areas. Autonomous mobile robotics is not an exception. EAs are utilized profusely for the purposes of localization and map building of unknown environment—SLAM. This paper concentrates on one particular class of EA, the so called differential evolution (DE). It addresses the problem of selecting a suitable set of parameter values for the DE algorithm applied to the task of continuous robot localization in a known environment under the presence of additive noise in sensorial data. The primary goal of this study is to find at least one type of perturbation vector from a set of known perturbation vector types, suitable to navigate a robot using 2D laser scanner (2DLS) sensorial system. The basic navigational algorithm used in this study uses a vector representation for both the data and the environment map, which is used as a reference data source for the navigation. Since the algorithm does not use a probability occupancy grid, the precision of the results is not limited by the grid resolution. The comparative study presented in this paper includes a relatively large amount of experiments in various types of environments. The results of the study suggest that the DE algorithm is a suitable tool for continuous robot localization task in an indoor environment, with or without moving objects, and under the presence of various levels of additive noise in sensorial data. Two perturbation vector types were found as the most suitable for this task on average, namely rand/1/exp and randtobest/1/bin.
      PubDate: 2013-09-25
       
  • Improved thresholding based on negative selection algorithm (NSA)
    • Abstract: Abstract Thresholding is a tool of image segmentation which groups the pixels in a logical way. In this paper, a novel algorithm based on negative selection algorithm a model of artificial immune system is proposed for image thresholding. The proposed algorithm is applied on the thresholded images of lathe tool produced using maximum information entropy (MIE) and global thresholding based technique resulting in an improved image. To verify the algorithm and results, it has also been applied on some of the inbuilt MATLAB (MATrix LABoratory) images. Histogram is employed to analyze the results. Further, the results of improved algorithm are compared with the results of MIE and the global thresholding methods to check the effectiveness of the proposed method. The experimental results confirm the potential of the developed algorithm.
      PubDate: 2013-09-01
       
 
 
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