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Diabetologia Notes de lecture     Hybrid Journal  
Diabetology Intl.     Hybrid Journal   (Followers: 1, SJR: 0.273, h-index: 5)
Dialectical Anthropology     Hybrid Journal   (Followers: 8, SJR: 0.314, h-index: 9)
Die Weltwirtschaft     Hybrid Journal   (Followers: 2)
Differential Equations     Hybrid Journal   (Followers: 2, SJR: 0.364, h-index: 15)
Differential Equations and Dynamical Systems     Hybrid Journal   (Followers: 1, SJR: 0.63, h-index: 7)
Digestive Diseases and Sciences     Hybrid Journal   (Followers: 4, SJR: 1.19, h-index: 89)
Directieve therapie     Hybrid Journal  
Discrete & Computational Geometry     Hybrid Journal   (Followers: 2, SJR: 1.269, h-index: 40)
Discrete Event Dynamic Systems     Hybrid Journal   (Followers: 2, SJR: 0.42, h-index: 32)
Distributed and Parallel Databases     Hybrid Journal   (Followers: 4, SJR: 0.766, h-index: 30)
Distributed Computing     Hybrid Journal   (Followers: 2, SJR: 1.41, h-index: 31)
DNP - Der Neurologe und Psychiater     Full-text available via subscription  
Documenta Ophthalmologica     Hybrid Journal   (Followers: 2, SJR: 0.946, h-index: 40)
Doklady Biochemistry and Biophysics     Hybrid Journal   (Followers: 2, SJR: 0.2, h-index: 10)
Doklady Biological Sciences     Hybrid Journal   (SJR: 0.248, h-index: 10)
Doklady Botanical Sciences     Hybrid Journal  
Doklady Chemistry     Hybrid Journal   (SJR: 0.272, h-index: 12)
Doklady Earth Sciences     Hybrid Journal   (SJR: 0.48, h-index: 17)
Doklady Mathematics     Hybrid Journal   (SJR: 0.345, h-index: 13)
Doklady Physical Chemistry     Hybrid Journal   (SJR: 0.299, h-index: 12)
Doklady Physics     Hybrid Journal   (Followers: 1, SJR: 0.293, h-index: 17)
Douleur et Analg├ęsie     Hybrid Journal   (SJR: 0.113, h-index: 6)
Drug Delivery and Translational Research     Hybrid Journal   (Followers: 2, SJR: 0.607, h-index: 8)
Drug Safety - Case Reports     Open Access  
Drugs : Real World Outcomes     Hybrid Journal   (Followers: 1)
Dynamic Games and Applications     Hybrid Journal   (Followers: 2, SJR: 0.481, h-index: 5)
Dysphagia     Hybrid Journal   (Followers: 235, SJR: 0.822, h-index: 52)
e & i Elektrotechnik und Informationstechnik     Hybrid Journal   (Followers: 9, SJR: 0.279, h-index: 9)
e-Neuroforum     Hybrid Journal  
Early Childhood Education J.     Hybrid Journal   (Followers: 13, SJR: 0.466, h-index: 16)
Earth Science Informatics     Hybrid Journal   (Followers: 3, SJR: 0.282, h-index: 7)
Earth, Moon, and Planets     Hybrid Journal   (Followers: 6, SJR: 0.303, h-index: 29)
Earthquake Engineering and Engineering Vibration     Hybrid Journal   (Followers: 7, SJR: 0.482, h-index: 21)
Earthquake Science     Hybrid Journal   (Followers: 8, SJR: 0.418, h-index: 9)
East Asia     Hybrid Journal   (Followers: 7, SJR: 0.18, h-index: 9)
Eating and Weight Disorders - Studies on Anorexia, Bulimia and Obesity     Hybrid Journal   (Followers: 9, SJR: 0.362, h-index: 27)
EcoHealth     Hybrid Journal   (Followers: 2, SJR: 0.88, h-index: 26)
Ecological Research     Hybrid Journal   (Followers: 8, SJR: 0.847, h-index: 43)
Economia e Politica Industriale     Hybrid Journal  
Economia Politica     Hybrid Journal   (SJR: 0.375, h-index: 6)
Economic Botany     Hybrid Journal   (Followers: 9, SJR: 0.527, h-index: 44)
Economic Bulletin     Hybrid Journal   (Followers: 4)
Economic Change and Restructuring     Hybrid Journal   (Followers: 1, SJR: 0.264, h-index: 9)
Economic Theory     Hybrid Journal   (Followers: 9, SJR: 2.557, h-index: 34)
Economic Theory Bulletin     Hybrid Journal   (Followers: 2)
Economics of Governance     Hybrid Journal   (Followers: 2, SJR: 0.408, h-index: 14)
Ecosystems     Hybrid Journal   (Followers: 19, SJR: 1.909, h-index: 93)
Ecotoxicology     Hybrid Journal   (Followers: 10, SJR: 1.333, h-index: 56)
Education and Information Technologies     Hybrid Journal   (Followers: 232, SJR: 0.366, h-index: 16)
Educational Assessment, Evaluation and Accountability     Hybrid Journal   (Followers: 17, SJR: 0.374, h-index: 15)
Educational Psychology Review     Hybrid Journal   (Followers: 15, SJR: 2.776, h-index: 61)
Educational Research for Policy and Practice     Hybrid Journal   (Followers: 7, SJR: 0.273, h-index: 9)
Educational Studies in Mathematics     Hybrid Journal   (Followers: 11, SJR: 0.825, h-index: 32)
Educational Technology Research and Development     Partially Free   (Followers: 219, SJR: 1.785, h-index: 52)
Electrical Engineering     Hybrid Journal   (Followers: 13, SJR: 0.336, h-index: 18)
Electrocatalysis     Hybrid Journal   (SJR: 0.883, h-index: 10)
Electronic Commerce Research     Hybrid Journal   (Followers: 4, SJR: 0.582, h-index: 16)
Electronic Markets     Hybrid Journal   (Followers: 5, SJR: 0.411, h-index: 8)
Electronic Materials Letters     Hybrid Journal   (Followers: 3, SJR: 1.407, h-index: 15)
Elemente der Mathematik     Hybrid Journal   (Followers: 1)
Emergency Radiology     Hybrid Journal   (Followers: 4, SJR: 0.678, h-index: 25)
Emission Control Science and Technology     Hybrid Journal  
Empirica     Hybrid Journal   (Followers: 3, SJR: 0.319, h-index: 16)
Empirical Economics     Hybrid Journal   (Followers: 8, SJR: 0.489, h-index: 31)
Empirical Software Engineering     Hybrid Journal   (Followers: 5, SJR: 1.285, h-index: 39)
Employee Responsibilities and Rights J.     Hybrid Journal   (Followers: 2, SJR: 0.361, h-index: 15)
Endocrine     Hybrid Journal   (Followers: 5, SJR: 0.878, h-index: 57)
Endocrine Pathology     Hybrid Journal   (Followers: 2, SJR: 0.638, h-index: 31)
Energy Efficiency     Hybrid Journal   (Followers: 11, SJR: 0.732, h-index: 14)
Energy Systems     Hybrid Journal   (Followers: 10, SJR: 1.176, h-index: 7)
Engineering With Computers     Hybrid Journal   (Followers: 5, SJR: 0.433, h-index: 30)
Entomological Review     Hybrid Journal   (Followers: 3, SJR: 0.144, h-index: 5)
Environment Systems & Decisions     Hybrid Journal   (Followers: 2)
Environment, Development and Sustainability     Hybrid Journal   (Followers: 29, SJR: 0.419, h-index: 29)
Environmental and Ecological Statistics     Hybrid Journal   (Followers: 5, SJR: 0.458, h-index: 32)
Environmental and Resource Economics     Hybrid Journal   (Followers: 18, SJR: 1.632, h-index: 54)
Environmental Biology of Fishes     Hybrid Journal   (Followers: 4, SJR: 0.725, h-index: 58)
Environmental Chemistry Letters     Hybrid Journal   (Followers: 2, SJR: 0.741, h-index: 28)
Environmental Earth Sciences     Hybrid Journal   (Followers: 11, SJR: 0.724, h-index: 63)
Environmental Economics and Policy Studies     Hybrid Journal   (Followers: 6, SJR: 0.524, h-index: 4)
Environmental Evidence     Open Access  
Environmental Fluid Mechanics     Hybrid Journal   (Followers: 2, SJR: 0.437, h-index: 24)
Environmental Geochemistry and Health     Hybrid Journal   (Followers: 2, SJR: 1.013, h-index: 36)
Environmental Geology     Hybrid Journal   (Followers: 11)
Environmental Health and Preventive Medicine     Hybrid Journal   (Followers: 3, SJR: 0.522, h-index: 19)
Environmental Management     Hybrid Journal   (Followers: 32, SJR: 0.942, h-index: 66)
Environmental Modeling & Assessment     Hybrid Journal   (Followers: 11, SJR: 0.533, h-index: 31)
Environmental Monitoring and Assessment     Hybrid Journal   (Followers: 8, SJR: 0.685, h-index: 52)
Environmental Science and Pollution Research     Hybrid Journal   (Followers: 14, SJR: 0.885, h-index: 46)
Epidemiologic Perspectives & Innovations     Open Access   (Followers: 3, SJR: 1.4, h-index: 17)
Epileptic Disorders     Hybrid Journal   (Followers: 1, SJR: 0.608, h-index: 38)
EPJ A - Hadrons and Nuclei     Hybrid Journal   (Followers: 1, SJR: 1.287, h-index: 63)
EPJ B - Condensed Matter and Complex Systems     Hybrid Journal   (Followers: 3, SJR: 0.731, h-index: 89)
EPJ direct     Hybrid Journal  
EPJ E - Soft Matter and Biological Physics     Hybrid Journal   (Followers: 1, SJR: 0.641, h-index: 62)
EPMA J.     Open Access   (SJR: 0.284, h-index: 6)
ERA-Forum     Hybrid Journal   (Followers: 2, SJR: 0.128, h-index: 3)
Erkenntnis     Hybrid Journal   (Followers: 13, SJR: 0.621, h-index: 16)
Erwerbs-Obstbau     Hybrid Journal   (SJR: 0.206, h-index: 9)

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Journal Cover   Evolutionary Intelligence
  [SJR: 0.409]   [H-I: 11]   Follow
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Print) 1864-5917 - ISSN (Online) 1864-5909
   Published by Springer-Verlag Homepage  [2302 journals]
  • XCS-SL: a rule-based genetic learning system for sequence labeling
    • Abstract: Abstract Sequence labeling is an interesting classification domain where, like normal classification, every input has a class label, but unlike normal classification, prediction of an input’s label may depend on the values of other inputs or their classes, and so a learner may need to refer to inputs and classes at different time stamps to classify the current input. This is more difficult because a learner does not know where and how many inputs are needed to classify the current input. Our interest is in learning general rules for sequence labeling. The XCS algorithm is a rule-based knowledge discovery system powered by a genetic algorithm which has often been used for classification. Here we present XCS-SL, an extension of XCS classifier system which can be applicable to sequence labeling. Towards an application of Learning Classifier System (LCS) to sequence labeling, we propose a new classifier condition with memory (called a variable-length condition) and a rule-discovery system for the new classifier condition, which enables XCS to apply it to sequence labeling. In XCS-SL, classification rules (called “classifiers” here) can include extra conditions on previous inputs, which act as memories. In sequence labeling, the number of conditions/memories needed may be different for each input, hence, using a fixed number of conditions (i.e., fixed-length condition) for all classifiers is not a good solution. Instead, XCS-SL classifiers have a variable-length condition to provide more or less memory. The genetic algorithm can grow and shrink conditions to find a suitable memory size. On two synthetic benchmark problems XCS-SL learns optimal classifiers, and on a real-world sequence labeling task it derives high classification accuracy and discovers interesting knowledge that shows dependencies between inputs at different times. The comprehensively described system is the first application of a LCS to sequence labeling and we consider it a promising direction for future work.
      PubDate: 2015-03-10
  • A hybrid evolutionary algorithm for the symbolic modeling of
           multiple-time-scale dynamical systems
    • Abstract: Abstract Natural and artificial dynamical systems in the real world often have dynamics at multiple time scales. Such dynamics can contribute substantially to the complexity of a dynamical system and increase the difficulty with which it can be analyzed. Although evolutionary algorithms have been proposed that are amenable to the automated modeling of dynamical systems, none have explicitly taken into account multiple time scales or leveraged the information about these dynamics that is inherent in experimental observations. We propose a hybrid approach to the design of models for multiple-time-scale dynamical systems that combines an evolutionary algorithm with other metaheuristics and conventional nonlinear regression. With only minimal human-supplied domain knowledge, the algorithm automates the process of analyzing raw experimental observations and creating an interpretable symbolic model of the system under study. We describe the algorithm in detail and demonstrate its applicability to a variety of both physical and simulated systems. In addition, we study the performance and scalability of the algorithm under different types of dynamics, varying levels of experimental noise, and other factors relevant to the practical application of the algorithm.
      PubDate: 2015-01-31
  • A brief history of learning classifier systems: from CS-1 to XCS and its
    • Abstract: Abstract The direction set by Wilson’s XCS is that modern Learning Classifier Systems can be characterized by their use of rule accuracy as the utility metric for the search algorithm(s) discovering useful rules. Such searching typically takes place within the restricted space of co-active rules for efficiency. This paper gives an overview of the evolution of Learning Classifier Systems up to XCS, and then of some of the subsequent developments of Wilson’s algorithm to different types of learning.
      PubDate: 2015-01-29
  • Turn-based evolution in a simplified model of artistic creative process
    • Abstract: Abstract Evolutionary computation has often been presented as a possible model for creativity in computers. In this paper, evolution is discussed in the light of a theoretical model of human artistic process, recently presented by the author. Some crucial differences between human artistic creativity and natural evolution are observed and discussed, also in the light of other creative processes occurring in nature. As a tractable way to overcome these limitations, a new kind of evolutionary implementation of creativity is proposed, based on a simplified version of the previously presented model, and the results of initial experiments are presented and discussed. Artistic creativity is here modeled as an iterated turn-based process, alternating between a conceptual representation and a material representation of the work-to-be. Evolutionary computation is proposed as a heuristic solution to the principal steps in this process, translating back and forth between the two kinds of representation. Those steps are: implementation, going from concept to material form, and re-conceptualization, forming a new conceptual representation based on the current material form. The advantages and disadvantages of this approach are discussed, and concluding from the initial experiments, it is a very promising path, well worth further exploration.
      PubDate: 2015-01-20
  • Special issue on creative intelligence
    • PubDate: 2015-01-13
  • Improvement of the performance of the Quantum-inspired Evolutionary
           Algorithms: structures, population, operators
    • Abstract: Abstract Population diversity is very important in giving the algorithm the power to explore the search space and not get trapped in local optima. In this respect, using a probabilistic representation for the quantum individuals, the Quantum-inspired Evolutionary Algorithms (QiEA) claim higher diversity in the population. Here, considering this important feature of QiEA, we propose different structures to offer better interaction between the q-individuals and propose new operators to preserve the diversity in the population and thus improve the performance of the QiEA. The effect of the structured population is investigated on the performance of the algorithm. Additionally, two operators are proposed in this paper. Being called the Diversity Preserving QiEA the first operator finds the converged similar q-individuals around a local optimum and while keeping the best q-individuals, by reinitializing the inferior ones pushes them out of the basin of attraction of the local optimum, so helping the algorithm to search other regions in the search space. The other operator is a reinitialization operator which by reinitializing the whole population helps it escape from the local optima it is trapped in. By studying the effect of the parameters of the proposed operators on their performance we show how the proposed operators improve the performance of QiEA. Experiments are performed on Knapsack, Trap and fourteen numerical objective functions and the results show better performance for the proposed algorithm than the original version of QiEA.
      PubDate: 2014-11-22
  • Monterey Mirror: an experiment in interactive music performance combining
           evolutionary computation and Zipf’s law
    • Abstract: Abstract Monterey Mirror is an experiment in interactive music performance. It is engages a human (the performer) and a computer (the mirror) in a game of playing, listening, and exchanging musical ideas. The computer side involves an interactive stochastic music generator which incorporates Markov models, genetic algorithms, and power-law metrics. This approach combines the predictive power of Markov models with the innovative power of genetic algorithms, using power-law metrics for fitness evaluation. These power-law metrics have been developed and refined in a decade-long project, which explores music information retrieval based on Zipf’s law and related power laws. We describe the architecture of Monterey Mirror, which can generate musical responses based on aesthetic variations of user input. We also explore how such a system may be used as a musical meta-instrument/environment in avant-garde music composition and performance projects.
      PubDate: 2014-11-21
  • Cooperative coevolutionary algorithms for dynamic optimization: an
           experimental study
    • Abstract: Abstract In this paper, we study the cooperative coevolutionary algorithms (CCEAs) for dynamic optimization. We introduce the CCEAs with two popular types of individuals: (1) random immigrants (RIs) that increase the diversity for changing environments, and (2) elitist individuals that increase the local convergence to the optima. The CCEAs are evaluated on a standard suite of benchmark problems and are compared with evolution strategies (ES). Our experimental results show that the CCEAs are efficient in locating and tracking optima in dynamic environments. They are superior to the ES when the RI individuals and the elitist individuals are used. In addition, we empirically investigate how the CCEAs perform with different parameter settings. These settings include collaboration methods, the use of plus–comma selections, and the number of RI individuals and elitist individuals. We also investigate the CCEAs that use a mutative σ-self adaptation. The CCEAs perform the best when they use the best collaboration method and the plus selection. The use of the mutative σ-self adaptation is insignificant. Our results also show that the CCEAs are more scalable than the ES in dynamic environments.
      PubDate: 2014-11-18
  • An evolutionary cognitive architecture made of a bag of networks
    • Abstract: Abstract A cognitive architecture is presented for modelling some properties of sensorimotor learning in infants, namely the ability to accumulate adaptations and skills over multiple tasks in a manner which allows recombination and re-use of task specific competences. The control architecture invented consists of a population of compartments (units of neuroevolution) each containing networks capable of controlling a robot with many degrees of freedom. The nodes of the network undergo internal mutations, and the networks undergo stochastic structural modifications, constrained by a mutational and recombinational grammar. The nodes used consist of dynamical systems such as dynamic movement primitives, continuous time recurrent neural networks and high-level supervised and unsupervised learning algorithms. Edges in the network represent the passing of information from a sending node to a receiving node. The networks in a compartment operate in parallel and encode a space of possible subsumption-like architectures that are used to successfully evolve a variety of behaviours for a NAO H25 humanoid robot.
      PubDate: 2014-11-15
  • Foreword
    • PubDate: 2014-11-15
  • A semantic network-based evolutionary algorithm for computational
    • Abstract: Abstract We introduce a novel evolutionary algorithm (EA) with a semantic network-based representation. For enabling this, we establish new formulations of EA variation operators, crossover and mutation, that we adapt to work on semantic networks. The algorithm employs commonsense reasoning to ensure all operations preserve the meaningfulness of the networks, using ConceptNet and WordNet knowledge bases. The algorithm can be interpreted as a novel memetic algorithm (MA), given that (1) individuals represent pieces of information that undergo evolution, as in the original sense of memetics as it was introduced by Dawkins; and (2) this is different from existing MA, where the word “memetic” has been used as a synonym for local refinement after global optimization. For evaluating the approach, we introduce an analogical similarity-based fitness measure that is computed through structure mapping. This setup enables the open-ended generation of networks analogous to a given base network.
      PubDate: 2014-11-13
  • NeuroEvolution: Evolving Heterogeneous Artificial Neural Networks
    • Abstract: Abstract NeuroEvolution is the application of Evolutionary Algorithms to the training of Artificial Neural Networks. Currently the vast majority of NeuroEvolutionary methods create homogeneous networks of user defined transfer functions. This is despite NeuroEvolution being capable of creating heterogeneous networks where each neuron’s transfer function is not chosen by the user, but selected or optimised during evolution. This paper demonstrates how NeuroEvolution can be used to select or optimise each neuron’s transfer function and empirically shows that doing so significantly aids training. This result is important as the majority of NeuroEvolutionary methods are capable of creating heterogeneous networks using the methods described.
      PubDate: 2014-11-08
  • Towards the evolution of vertical-axis wind turbines using supershapes
    • Abstract: Abstract We have recently presented an initial study of evolutionary algorithms used to design vertical-axis wind turbines (VAWTs) wherein candidate prototypes are evaluated under fan generated wind conditions after being physically instantiated by a 3D printer. That is, unlike other approaches such as computational fluid dynamics simulations, no mathematical formulations are used and no model assumptions are made. However, the representation used significantly restricted the range of morphologies explored. In this paper, we present initial explorations into the use of a simple generative encoding, known as Gielis superformula, that produces a highly flexible 3D shape representation to design VAWT. First, the target-based evolution of 3D artefacts is investigated and subsequently initial design experiments are performed wherein each VAWT candidate is physically instantiated and evaluated under fan generated wind conditions. It is shown possible to produce very closely matching designs of a number of 3D objects through the evolution of supershapes produced by Gielis superformula. Moreover, it is shown possible to use artificial physical evolution to identify novel and increasingly efficient supershape VAWT designs.
      PubDate: 2014-10-28
  • 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
    • 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
    • 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
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