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Computational Intelligence (UKCI), 2013 13th UK Workshop on

Date 9-11 Sept. 2013

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Displaying Results 1 - 25 of 56
  • [Front cover]

    Publication Year: 2013 , Page(s): 1
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  • [Blank page]

    Publication Year: 2013 , Page(s): 1
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  • [Title page]

    Publication Year: 2013 , Page(s): 1
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  • [Copyright notice]

    Publication Year: 2013 , Page(s): 1
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  • Foreword

    Publication Year: 2013 , Page(s): iii
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  • Organisation committee

    Publication Year: 2013 , Page(s): iv - v
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  • Table of contents

    Publication Year: 2013 , Page(s): vi - viii
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  • Evolving gene regulatory networks with mobile DNA mechanisms

    Publication Year: 2013 , Page(s): 1 - 7
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (640 KB) |  | HTML iconHTML  

    This paper uses a recently presented abstract, tuneable Boolean regulatory network model extended to consider aspects of mobile DNA, such as transposons. The significant role of mobile DNA in the evolution of natural systems is becoming increasingly clear. This paper shows how dynamically controlling network node connectivity and function via transposon-inspired mechanisms can be selected for in computational intelligence tasks to give improved performance. The designs of dynamical networks intended for implementation within the slime mould Physarum polycephalum and for the distributed control of a smart surface are considered. View full abstract»

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  • Combining biochemical network motifs within an ARN-agent control system

    Publication Year: 2013 , Page(s): 8 - 15
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (459 KB) |  | HTML iconHTML  

    The Artificial Reaction Network (ARN) is an Artificial Chemistry representation inspired by cell signaling networks. The ARN has previously been applied to the simulation of the chemotaxis pathway of Escherichia coli and to the control of limbed robots. In this paper we discuss the design of an ARN control system composed of a combination of network motifs found in actual biochemical networks. Using this control system we create multiple cell-like autonomous agents capable of coordinating all aspects of their behavior, recognizing environmental patterns and communicating with other agent's stigmergically. The agents are applied to simulate two phases of the life cycle of Dictyostelium discoideum: vegetative and aggregation phase including the transition. The results of the simulation show that the ARN is well suited for construction of biochemical regulatory networks. Furthermore, it is a powerful tool for modeling multi agent systems such as a population of amoebae or bacterial colony. View full abstract»

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  • Evolving neural networks using ant colony optimization with pheromone trail limits

    Publication Year: 2013 , Page(s): 16 - 23
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    The back-propagation (BP) technique is a widely used technique to train artificial neural networks (ANNs). However, BP often gets trapped in a local optimum. Hence, hybrid training was introduced, e.g., a global optimization algorithm with BP, to address this drawback. The key idea of hybrid training is to use global optimization algorithms to provide BP with good initial connection weights. In hybrid training, evolutionary algorithms are widely used, whereas ant colony optimization (ACO) algorithms are rarely used, as the global optimization algorithms. And so far, only the basic ACO algorithm has been used to evolve the connection weights of ANNs. In this paper, we hybridize one of the best performing variations of ACO with BP. The difference of the improved ACO variation from the basic ACO algorithm lies in that pheromone trail limits are imposed to avoid stagnation behaviour. The experimental results show that the proposed training method outperforms other peer training methods. View full abstract»

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  • Reconstructing regulatory networks in Streptomyces using evolutionary algorithms

    Publication Year: 2013 , Page(s): 24 - 30
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1063 KB) |  | HTML iconHTML  

    Reconstructing biological networks is vital in developing our understanding of nature. Biological systems of particular interest are bacteria that can produce antibiotics during their life cycle. Such an organism is the soil dwelling bacterium Streptomyces coelicolor. Although some of the genes involved in the production of antibiotics in the bacterium have been identified, how these genes are regulated and their specific role in antibiotic production is unknown. By understanding the network structure and gene regulation involved it may be possible to improve the production of antibiotics from this bacterium. Here we use an evolutionary algorithm to optimise parameters in the gene regulatory network of a sub-set of genes in S. coelicolor involved in antibiotic production. We present some of our preliminary results based on real gene expression data for continuous and discrete modelling techniques. View full abstract»

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  • Stepwise modelling of biochemical pathways based on qualitative model learning

    Publication Year: 2013 , Page(s): 31 - 37
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1132 KB) |  | HTML iconHTML  

    Modelling of biochemical pathways in a computational way has received considerable attention over the last decade from biochemistry, computing sciences, and mathematics. In this paper we present an approach to evolutionarily stepwise constructing models of biochemical pathways by a qualitative model learning methodology. Given a set of reactants involved in a target biochemical pathway, atomic components can be generated and preserved in a components library for further model composition. These synthetic components are then reused to compose models which are qualitatively evaluated by referring to experimental qualitative states of the given reactants. Simulation results show that our stepwise evolutionary qualitative model learning approach can learn the relationships among reactants in biochemical pathway, by exploring topology space of alternative models. In addition, synthetic biochemical complex can be obtained as hidden reactants in composed models. The inferred hidden reactants and topologies of the synthetic models can be further investigated by biologists in experimental environment for understanding biological principles. View full abstract»

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  • Measuring the directional distance between fuzzy sets

    Publication Year: 2013 , Page(s): 38 - 45
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1388 KB) |  | HTML iconHTML  

    The measure of distance between two fuzzy sets is a fundamental tool within fuzzy set theory. However, current distance measures within the literature do not account for the direction of change between fuzzy sets; a useful concept in a variety of applications, such as Computing With Words. In this paper, we highlight this utility and introduce a distance measure which takes the direction between sets into account. We provide details of its application for normal and non-normal, as well as convex and non-convex fuzzy sets. We demonstrate the new distance measure using real data from the MovieLens dataset and establish the benefits of measuring the direction between fuzzy sets. View full abstract»

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  • Fuzzy interpolation and extrapolation using shift ratio and overall weight measurement based on areas of fuzzy sets

    Publication Year: 2013 , Page(s): 46 - 53
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1002 KB) |  | HTML iconHTML  

    Conventional fuzzy reasoning methods requires compact fuzzy rule base to infer a result, but due to incomplete data or lack of expertise knowledge, compact rule bases are not always available. Fuzzy interpolation methods have been widely researched to reasonably allow the interpolation a fuzzy result using the nearest available rules. Chang et al. [24] proposed a novel interpolation method which employs the weighted average on the area of the fuzzy set. However, the interpolated observation does not fully represent the actual observation that is given. In our proposed extension to this method, a different weight computation and a shift technique are included to ensure that the normal point of the observation and the normal point of the interpolated observation are mapped together. This weight computation and shift technique has also enabled the capability of extrapolation to be performed implicitly. View full abstract»

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  • Comparison of crisp systems and fuzzy systems in agent-based simulation: A case study of soccer penalties

    Publication Year: 2013 , Page(s): 54 - 61
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (473 KB) |  | HTML iconHTML  

    The Belief-Desire-Intention (BDI) software model is an example of a reasoning architecture for a bounded rational software agent. In our research we plan to expand the application of the BDI software model to the area of simulating human behaviour in social and socio-technical systems. To this effect, in this paper we explore the differences in using a classical crisp rule-based approach and a fuzzy rule-based approach for the reasoning within the BDI system. As a test case we have chosen a football penalty shootout. We have kept the case study example deliberately simple so that we can focus on the effects the different BDI implementations have on the decisions made. Our experiments highlight that the crisp system can result in unwanted “preferred” actions because of sudden leaps or drops between different ranges of decision variables, while the fuzzy system results have smoother transitions which results in more consistent decisions. The behaviour, as showcased in this simple context, underlines that a change from crisp to fuzzy rule based systems as the underlying reasoning model in BDI systems can provide the path to a superior approach for the simulation of human behaviour, which we will explore further in the future. View full abstract»

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  • Minkowski compactness measure

    Publication Year: 2013 , Page(s): 62 - 66
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1098 KB) |  | HTML iconHTML  

    Many compactness measures are available in the literature. In this paper we present a generalised compactness measure Cq(S) which unifies previously existing definitions of compactness. The new measure is based on Minkowski distances and incorporates a parameter q which modifies the behaviour of the compactness measure. Different shapes are considered to be most compact depending on the value of q: for q = 2, the most compact shape in 2D (3D) is a circle (a sphere); for q→∞, the most compact shape is a square (a cube); and for q = 1, the most compact shape is a square (a octahedron). For a given shape S, measure Cq(S) can be understood as a function of q and as such it is possible to calculate a spectum of Cq(S) for a range of q. This produces a particular compactness signature for the shape S, which provides additional shape information. The experiments section of this paper provides illustrative examples where measure Cq(S) is applied to various shapes and describes how measure and its spectrum can be used for image processing applications. View full abstract»

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  • The X-mu representation of fuzzy sets — Regaining the excluded middle

    Publication Year: 2013 , Page(s): 67 - 73
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (354 KB) |  | HTML iconHTML  

    Fuzzy sets are a good model of the flexible definitions used in human language, but are not always in accordance with human reasoning because they do not satisfy the law of the excluded middle. In this paper, we outline the X-μ approach, a new method of representing, visualizing and calculating functions of fuzzy quantities. Using simple examples, we illustrate that the law of the excluded middle is satisfied with the X-μ approach, although it is not always possible to recover standard membership functions from the results of a calculation. View full abstract»

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  • Towards the evolution of novel vertical-axis wind turbines

    Publication Year: 2013 , Page(s): 74 - 81
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1499 KB) |  | HTML iconHTML  

    Renewable and sustainable energy is one of the most important challenges currently facing mankind. Wind has made an increasing contribution to the world's energy supply mix, but still remains a long way from reaching its full potential. In this paper, we investigate the use of artificial evolution to design vertical-axis wind turbine prototypes that are physically instantiated and evaluated under approximated wind tunnel conditions. An artificial neural network is used as a surrogate model to assist learning and found to reduce the number of fabrications required to reach a higher aerodynamic efficiency. Unlike in other approaches, such as computational fluid dynamics simulations, no mathematical formulations are used and no model assumptions are made. View full abstract»

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  • Simulating swarm behaviuors for optimisation by learning from neighbours

    Publication Year: 2013 , Page(s): 82 - 87
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1248 KB) |  | HTML iconHTML  

    Competitive particle swarm optimizer (ComPSO) is a novel swarm intelligence algorithm that does not need any memory. Different from the canonical particle swarm optimizer (PSO), neither gbest nor pbest needs to be stored in ComPSO, and the algorithm is extremely simple in implementation. ComPSO has shown to be highly scalable to the search dimension. In the original ComPSO, two particles are randomly chosen to compete. This work investigates the influence of the competition rule on the search performance of ComPSO and proposes a new competition rule operating on a sorted swarm with neighborhood control. Empirical studies have been performed on a set of widely used test functions to compare the new competition rule with the random strategy. Results show that the new competition rule can speed up the convergence with a big neighborhood size, while with a small neighborhood size, the convergence speed can be slowed down. View full abstract»

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  • Multi-modal optimisation using a localised surrogates assisted evolutionary algorithm

    Publication Year: 2013 , Page(s): 88 - 95
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (6024 KB) |  | HTML iconHTML  

    There has been a steady growth in interest in niching approaches within the evolutionary computation community, as an increasing number of real world problems are discovered that exhibit multi-modality of varying degrees of intensity (modes). It is often useful to locate and memorise the modes encountered - this is because the optimal decision parameter combinations discovered may not be feasible when moving from a mathematical model emulating the real problem to engineering an actual solution, or the model may be in error in some regions. As such a range of disparate modal solutions is of practical use. This paper investigates the use of a collection of localised surrogate models for niche/mode discovery, and analyses the performance of a novel evolutionary algorithm (EA) which embeds these surrogates into its search process. Results obtained are compared to the published performance of state-of-the-art evolutionary algorithms developed for multi-modal problems. We find that using a collection of localised surrogates not only makes the problem tractable from a model-fitting viewpoint, it also produces competitive results with other EA approaches. View full abstract»

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  • Set-based genetic algorithms for solving many-objective optimization problems

    Publication Year: 2013 , Page(s): 96 - 103
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (365 KB) |  | HTML iconHTML  

    Many-objective optimization problems are very common and important in real-world applications, and there exist few methods suitable for them. Therefore, many-objective optimization problems are focused on in this study, and a set-based genetic algorithm is presented to effectively solve them. First, each objective of the original optimization problem is transformed into a desirability function according to the preferred region defined by the decision-maker. Thereafter, the transformed problem is further converted to a bi-objective optimization one by taking hyper-volume and the decision-maker's satisfaction as the new objectives, and a set of solutions of the original optimization problem as the new decision variable. To tackle the converted bi-objective optimization problem by using genetic algorithms, the crossover operator inside a set is designed based on the simplex method by using solutions of the original optimization problem, and the crossover operator between sets is developed by using the entropy of sets. In addition, the mutation operator of a set is presented to obey the Gaussian distribution and change along with the decision-maker's preferences. The proposed method is applied to five benchmark many-objective optimization problems, and compared with other six methods. The experimental results empirically demonstrate its effectiveness. View full abstract»

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  • Large-scale optimization: Are co-operative co-evolution and fitness inheritance additive?

    Publication Year: 2013 , Page(s): 104 - 111
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (364 KB) |  | HTML iconHTML  

    Large-scale optimization - here referring mainly to problems with many design parameters - remains a serious challenge for optimization algorithms. When the problem at hand does not succumb to analytical treatment (an overwhelmingly commonplace situation), the engineering and adaptation of stochastic black box optimization methods tends to be a favoured approach, particularly the use of Evolutionary Algorithms (EAs). In this context, many approaches are currently under investigation for accelerating performance on large-scale problems, and we focus on two of those in this paper. The first is co-operative co-evolution (CC), where the strategy is to successively optimize only subsets of the design parameters at a time, keeping the remainder fixed, with an organized approach to managing and reconciling these `subspace' optimizations. The second is fitness inheritance (FI), which is essentially a very simple surrogate model strategy, in which, with some probability, the fitness of a solution is simply guessed to be a simple function of the fitnesses of that solution's `parents'. Both CC and FI have been found successful on nontrivial and multiple test cases, and they use fundamentally distinct strategies. In this article we explore the extent to which employing both of these strategies at once provides additional benefit. Based on experiments with 50D-1000D variants of four test functions, we find `CCEA-FI' to be highly effective, especially when a random grouping scheme is used in the CC component. View full abstract»

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  • Maximal-margin case-based inference

    Publication Year: 2013 , Page(s): 112 - 119
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (945 KB) |  | HTML iconHTML  

    The central problem in case-based reasoning (CBR) is to produce a solution for a new problem instance by using a set of existing problem-solution cases. The basic heuristic guiding CBR is the assumption that similar problems have similar solutions. CBR has been often criticized for lacking a sound theoretical basis, and there has only recently been some attempts at developing a theoretical framework, including recent work by Hullermeier, who made a link between CBR and the probably approximately correct (or PAC) probabilistic model of learning in his `case-based inference' (CBI) formulation. In this paper we present a new framework of CBI which models it as a multi-category classification problem. We use a recently-developed notion of geometric margin of classification to obtain generalization error bounds. View full abstract»

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  • State detection from electromyographic signals towards the control of prosthetic limbs

    Publication Year: 2013 , Page(s): 120 - 127
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (730 KB) |  | HTML iconHTML  

    This paper presents experiments in the use of an Electromyographic sensor to determine whether a person is standing, walking or running. The output of the sensor was captured and processed in a variety of different ways to extract those features that were seen to be changing as the movement state of the person changed. Experiments were carried out by adjusting the parameters used for the collection of the features. These extracted features where then passed to a set of Artificial Neural Networks trained to recognise each state. This methodology exhibits an accuracy needed to control a prosthetic leg. View full abstract»

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  • Partial structure learning by subset walsh transform

    Publication Year: 2013 , Page(s): 128 - 135
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (957 KB) |  | HTML iconHTML  

    Estimation of distribution algorithms (EDAs) use structure learning to build a statistical model of good solutions discovered so far, in an effort to discover better solutions. The non-zero coefficients of the Walsh transform produce a hypergraph representation of structure of a binary fitness function; however, computation of all Walsh coefficients requires exhaustive evaluation of the search space. In this paper, we propose a stochastic method of determining Walsh coefficients for hyperedges contained within the selected subset of the variables (complete local structure). This method also detects parts of hyperedges which cut the boundary of the selected variable set (partial structure), which may be used to incrementally build an approximation of the problem hypergraph. View full abstract»

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