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Towards Learning of Filter-Level Heterogeneous Compression of Convolutional Neural NetworksApr 22 2019Recently, deep learning has become a de facto standard in machine learning with convolutional neural networks (CNNs) demonstrating spectacular success on a wide variety of tasks. However, CNNs are typically very demanding computationally at inference ... More

Algorithm Portfolio for Individual-based Surrogate-Assisted Evolutionary AlgorithmsApr 22 2019Surrogate-assisted evolutionary algorithms (SAEAs) are powerful optimisation tools for computationally expensive problems (CEPs). However, a randomly selected algorithm may fail in solving unknown problems due to no free lunch theorems, and it will cause ... More

Donkey and Smuggler Optimization Algorithm: A Collaborative Working Approach to Path FindingApr 19 2019Swarm Intelligence is a metaheuristic optimization approach that has become very predominant over the last few decades. These algorithms are inspired by animals' physical behaviors and their evolutionary perceptions. The simplicity of these algorithms ... More

Continual Learning with Self-Organizing MapsApr 19 2019Despite remarkable successes achieved by modern neural networks in a wide range of applications, these networks perform best in domain-specific stationary environments where they are trained only once on large-scale controlled data repositories. When ... More

SCANN: Synthesis of Compact and Accurate Neural NetworksApr 19 2019Artificial neural networks (ANNs) have become the driving force behind recent artificial intelligence (AI) research. An important problem with implementing a neural network is the design of its architecture. Typically, such an architecture is obtained ... More

Intentional Computational Level DesignApr 18 2019The procedural generation of levels and content in video games is a challenging AI problem. Often such generation relies on an intelligent way of evaluating the content being generated so that constraints are satisfied and/or objectives maximized. In ... More

On the validity of memristor modeling in the neural network literatureApr 18 2019An analysis of the literature shows that there are two types of non-memristive models that have been widely used in the modeling of so-called "memristive" neural networks. Here, we demonstrate that such models have nothing in common with the concept of ... More

Uncrowded Hypervolume Improvement: COMO-CMA-ES and the Sofomore frameworkApr 18 2019We present a framework to build a multiobjective algorithm from single-objective ones. This framework addresses the $p \times n$-dimensional problem of finding p solutions in an n-dimensional search space, maximizing an indicator by dynamic subspace optimization. ... More

Interplanetary Transfers via Deep Representations of the Optimal Policy and/or of the Value FunctionApr 18 2019A number of applications to interplanetary trajectories have been recently proposed based on deep networks. These approaches often rely on the availability of a large number of optimal trajectories to learn from. In this paper we introduce a new method ... More

Batch Tournament Selection for Genetic ProgrammingApr 18 2019Lexicase selection achieves very good solution quality by introducing ordered test cases. However, the computational complexity of lexicase selection can prohibit its use in many applications. In this paper, we introduce Batch Tournament Selection (BTS), ... More

Semantic variation operators for multidimensional genetic programmingApr 18 2019Multidimensional genetic programming represents candidate solutions as sets of programs, and thereby provides an interesting framework for exploiting building block identification. Towards this goal, we investigate the use of machine learning as a way ... More

MorphIC: A 65-nm 738k-Synapse/mm$^2$ Quad-Core Binary-Weight Digital Neuromorphic Processor with Stochastic Spike-Driven Online LearningApr 17 2019Recent trends in the field of artificial neural networks (ANNs) and convolutional neural networks (CNNs) investigate weight quantization as a means to increase the resource- and power-efficiency of hardware devices. As full on-chip weight storage is necessary ... More

Dynamic Evaluation of Transformer Language ModelsApr 17 2019This research note combines two methods that have recently improved the state of the art in language modeling: Transformers and dynamic evaluation. Transformers use stacked layers of self-attention that allow them to capture long range dependencies in ... More

An Exponential Lower Bound for the Runtime of the cGA on Jump FunctionsApr 17 2019In the first runtime analysis of an estimation-of-distribution algorithm (EDA) on the multi-modal jump function class, Hasen\"ohrl and Sutton (GECCO 2018) proved that the runtime of the compact genetic algorithm with suitable parameter choice on jump ... More

Bayesian policy selection using active inferenceApr 17 2019Learning to take actions based on observations is a core requirement for artificial agents to be able to be successful and robust at their task. Reinforcement Learn-ing (RL) is a well-known technique for learning such policies. However, current RL algorithms ... More

Sparseout: Controlling Sparsity in Deep NetworksApr 17 2019Dropout is commonly used to help reduce overfitting in deep neural networks. Sparsity is a potentially important property of neural networks, but is not explicitly controlled by Dropout-based regularization. In this work, we propose Sparseout a simple ... More

Offspring Population Size Matters when Comparing Evolutionary Algorithms with Self-Adjusting Mutation RatesApr 17 2019We analyze the performance of the 2-rate $(1+\lambda)$ Evolutionary Algorithm (EA) with self-adjusting mutation rate control, its 3-rate counterpart, and a $(1+\lambda)$~EA variant using multiplicative update rules on the OneMax problem. We compare their ... More

Offspring Population Size Matters when Comparing Evolutionary Algorithms with Self-Adjusting Mutation RatesApr 17 2019Apr 18 2019We analyze the performance of the 2-rate $(1+\lambda)$ Evolutionary Algorithm (EA) with self-adjusting mutation rate control, its 3-rate counterpart, and a $(1+\lambda)$~EA variant using multiplicative update rules on the OneMax problem. We compare their ... More

3D Shape Synthesis for Conceptual Design and Optimization Using Variational AutoencodersApr 16 2019We propose a data-driven 3D shape design method that can learn a generative model from a corpus of existing designs, and use this model to produce a wide range of new designs. The approach learns an encoding of the samples in the training corpus using ... More

Response of Selective Attention in Middle Temporal AreaApr 16 2019The primary visual cortex processes a large amount of visual information, however, due to its large receptive fields, when multiple stimuli fall within one receptive field, there are computational problems. To solve this problem, the visual system uses ... More

Maximizing Drift is Not Optimal for Solving OneMaxApr 16 2019It seems very intuitive that for the maximization of the OneMax problem $f(x):=\sum_{i=1}^n{x_i}$ the best that an elitist unary unbiased search algorithm can do is to store a best so far solution, and to modify it with the operator that yields the best ... More

Online Selection of CMA-ES VariantsApr 16 2019In the field of evolutionary computation, one of the most challenging topics is algorithm selection. Knowing which heuristics to use for which optimization problem is key to obtaining high-quality solutions. We aim to extend this research topic by taking ... More

Applying Partial-ACO to Large-scale Vehicle Fleet OptimisationApr 16 2019Optimisation of fleets of commercial vehicles with regards scheduling tasks from various locations to vehicles can result in considerably lower fleet traversal times. This has significant benefits including reduced expenses for the company and more importantly, ... More

The 1/5-th Rule with Rollbacks: On Self-Adjustment of the Population Size in the $(1+(λ,λ))$ GAApr 15 2019Self-adjustment of parameters can significantly improve the performance of evolutionary algorithms. A notable example is the $(1+(\lambda,\lambda))$ genetic algorithm, where the adaptation of the population size helps to achieve the linear runtime on ... More

An LGMD Based Competitive Collision Avoidance Strategy for UAVApr 15 2019Building a reliable and efficient collision avoidance system for unmanned aerial vehicles (UAVs) is still a challenging problem. This research takes inspiration from locusts, which can fly in dense swarms for hundreds of miles without collision. In the ... More

Synthetic Neural Vision System Design for Motion Pattern Recognition in Dynamic Robot ScenesApr 15 2019Insects have tiny brains but complicated visual systems for motion perception. A handful of insect visual neurons have been computationally modeled and successfully applied for robotics. How different neurons collaborate on motion perception, is an open ... More

A Hybrid Evolutionary Algorithm Framework for Optimising Power Take Off and Placements of Wave Energy ConvertersApr 15 2019Ocean wave energy is a source of renewable energy that has gained much attention for its potential to contribute significantly to meeting the global energy demand. In this research, we investigate the problem of maximising the energy delivered by farms ... More

Improved Precision and Recall Metric for Assessing Generative ModelsApr 15 2019The ability to evaluate the performance of a computational model is a vital requirement for driving algorithm research. This is often particularly difficult for generative models such as generative adversarial networks (GAN) that model a data manifold ... More

The Efficiency Threshold for the Offspring Population Size of the ($μ$, $λ$) EAApr 15 2019Understanding when evolutionary algorithms are efficient or not, and how they efficiently solve problems, is one of the central research tasks in evolutionary computation. In this work, we make progress in understanding the interplay between parent and ... More

Efficient Feature Selection of Power Quality Events using Two Dimensional (2D) Particle SwarmsApr 15 2019A novel two-dimensional (2D) learning framework has been proposed to address the feature selection problem in Power Quality (PQ) events. Unlike the existing feature selection approaches, the proposed 2D learning explicitly incorporates the information ... More

On the Performance of Differential Evolution for Hyperparameter TuningApr 15 2019Automated hyperparameter tuning aspires to facilitate the application of machine learning for non-experts. In the literature, different optimization approaches are applied for that purpose. This paper investigates the performance of Differential Evolution ... More

A Reference Vector based Many-Objective Evolutionary Algorithm with Feasibility-aware AdaptationApr 12 2019The infeasible parts of the objective space in difficult many-objective optimization problems cause trouble for evolutionary algorithms. This paper proposes a reference vector based algorithm which uses two interacting engines to adapt the reference vectors ... More

Locally Connected Spiking Neural Networks for Unsupervised Feature LearningApr 12 2019In recent years, Spiking Neural Networks (SNNs) have demonstrated great successes in completing various Machine Learning tasks. We introduce a method for learning image features by \textit{locally connected layers} in SNNs using spike-timing-dependent ... More

Evolving Indoor Navigational Strategies Using Gated Recurrent Units In NEATApr 12 2019Simultaneous Localisation and Mapping (SLAM) algorithms are expensive to run on smaller robotic platforms such as Micro-Aerial Vehicles. Bug algorithms are an alternative that use relatively little processing power, and avoid high memory consumption by ... More

On the Impact of the Cutoff Time on the Performance of Algorithm ConfiguratorsApr 12 2019Algorithm configurators are automated methods to optimise the parameters of an algorithm for a class of problems. We evaluate the performance of a simple random local search configurator (ParamRLS) for tuning the neighbourhood size $k$ of the RLS$_k$ ... More

Evolved Art with Transparent, Overlapping, and Geometric ShapesApr 12 2019In this work, an evolutionary art project is presented where images are approximated by transparent, overlapping and geometric shapes of different types, e.g., polygons, circles, lines. Genotypes representing features and order of the geometric shapes ... More

Compressing deep neural networks by matrix product operatorsApr 11 2019A deep neural network is a parameterization of a multi-layer mapping of signals in terms of many alternatively arranged linear and nonlinear transformations. The linear transformations, which are generally used in the fully-connected as well as convolutional ... More

GP-HD: Using Genetic Programming to Generate Dynamical Systems Models for Health CareApr 11 2019The huge wealth of data in the health domain can be exploited to create models that predict development of health states over time. Temporal learning algorithms are well suited to learn relationships between health states and make predictions about their ... More

Scalarizing Functions in Bayesian Multiobjective OptimizationApr 11 2019Scalarizing functions have been widely used to convert a multiobjective optimization problem into a single objective optimization problem. However, their use in solving (computationally) expensive multi- and many-objective optimization problems in Bayesian ... More

Multiplicative Up-DriftApr 11 2019Drift analysis aims at translating the expected progress of an evolutionary algorithm (or more generally, a random process) into a probabilistic guarantee on its run time (hitting time). So far, drift arguments have been successfully employed in the rigorous ... More

Multi-lingual Dialogue Act Recognition with Deep Learning MethodsApr 11 2019This paper deals with multi-lingual dialogue act (DA) recognition. The proposed approaches are based on deep neural networks and use word2vec embeddings for word representation. Two multi-lingual models are proposed for this task. The first approach uses ... More

Fitness Dependent Optimizer: Inspired by the Bee Swarming Reproductive ProcessApr 10 2019In this paper, a novel swarm intelligent algorithm is proposed, known as the fitness dependent optimizer (FDO). The bee swarming reproductive process and their collective decision-making have inspired this algorithm; it has no algorithmic connection with ... More

Multitask Hopfield NetworksApr 10 2019Multitask algorithms typically use task similarity information as a bias to speed up and improve the performance of learning processes. Tasks are learned jointly, sharing information across them, in order to construct models more accurate than those learned ... More

A review on Neural Turing MachineApr 10 2019One of the major objectives of Artificial Intelligence is to design learning algorithms that are executed on a general purposes computational machines such as human brain. Neural Turing Machine (NTM) is a step towards realizing such a computational machine. ... More

Classification of Two-channel Signals by Means of Genetic ProgrammingApr 10 2019Traditionally, signal classification is a process in which previous knowledge of the signals is needed. Human experts decide which features are extracted from the signals, and used as inputs to the classification system. This requirement can make significant ... More

An Interactive Musical Prediction System with Mixture Density Recurrent Neural NetworksApr 10 2019This paper is about creating digital musical instruments where a predictive neural network model is integrated into the interactive system. Rather than predicting symbolic music (e.g., MIDI notes), we suggest that predicting future control data from the ... More

Black-Box Complexity of the Binary Value FunctionApr 09 2019The binary value function, or BinVal, has appeared in several studies in theory of evolutionary computation as one of the extreme examples of linear pseudo-Boolean functions. Its unbiased black-box complexity was previously shown to be at most $\lceil ... More

Embodied Event-Driven Random BackpropagationApr 09 2019Spike-based communication between biological neurons is sparse and unreliable. This enables the brain to process visual information from the eyes efficiently. Taking inspiration from biology, artificial spiking neural networks coupled with silicon retinas ... More

A Hybrid Evolutionary System for Automated Artificial Neural Networks Generation and Simplification in Biomedical ApplicationsApr 09 2019Data mining and data classification over biomedical data are two of the most important research fields in computer science. Among the great diversity of techniques that can be used for this purpose, Artifical Neural Networks (ANNs) is one of the most ... More

Software and application patterns for explanation methodsApr 09 2019Deep neural networks successfully pervaded many applications domains and are increasingly used in critical decision processes. Understanding their workings is desirable or even required to further foster their potential as well as to access sensitive ... More

Hyper-Parameter Tuning for the (1+(λ,λ)) GAApr 09 2019It is known that the $(1+(\lambda,\lambda))$~Genetic Algorithm (GA) with self-adjusting parameter choices achieves a linear expected optimization time on OneMax if its hyper-parameters are suitably chosen. However, it is not very well understood how the ... More

A Feature-Value Network as a Brain ModelApr 09 2019This paper suggests a statistical framework for describing the relations between the physical and conceptual entities of a brain-like model. In particular, features and concept instances are put into context. This may help with understanding or implementing ... More

Deep CytometryApr 09 2019Deep learning has achieved spectacular performance in image and speech recognition and synthesis. It outperforms other machine learning algorithms in problems where large amounts of data are available. In the area of measurement technology, instruments ... More

Lecturer Performance System Using Neural Network with Particle Swarm OptimizationApr 08 2019The field of analyzing performance is very important and sensitive in particular when it is related to the performance of lecturers in academic institutions. Locating the weak points of lecturers through a system that provides an early warning to notify ... More

Characterizing the Social Interactions in the Artificial Bee Colony AlgorithmApr 08 2019Computational swarm intelligence consists of multiple artificial simple agents exchanging information while exploring a search space. Despite a rich literature in the field, with works improving old approaches and proposing new ones, the mechanism by ... More

Nucleus Neural Network for Super Robust LearningApr 08 2019Artificial neural networks which model the neurons and connecting architectures in brain have achieved great successes in many problems, especially those with deep layers. In this paper, we propose a nucleus neural network (NNN) and corresponding architecture ... More

Towards Real-Time Automatic Portrait Matting on Mobile DevicesApr 08 2019We tackle the problem of automatic portrait matting on mobile devices. The proposed model is aimed at attaining real-time inference on mobile devices with minimal degradation of model performance. Our model MMNet, based on multi-branch dilated convolution ... More

Temporal Convolution for Real-time Keyword Spotting on Mobile DevicesApr 08 2019Keyword spotting (KWS) plays a critical role in enabling speech-based user interactions on smart devices. Recent developments in the field of deep learning have led to wide adoption of convolutional neural networks (CNNs) in KWS systems due to their exceptional ... More

Every Local Minimum is a Global Minimum of an Induced ModelApr 07 2019For non-convex optimization in machine learning, this paper proves that every local minimum achieves the global optimality of the perturbable gradient basis model at any differentiable point. As a result, non-convex machine learning is theoretically as ... More

Human Intracranial EEG Quantitative Analysis and Automatic Feature Learning for Epileptic Seizure PredictionApr 07 2019Objective: The aim of this study is to develop an efficient and reliable epileptic seizure prediction system using intracranial EEG (iEEG) data, especially for people with drug-resistant epilepsy. The prediction procedure should yield accurate results ... More

A Compendium on Network and Host based Intrusion Detection SystemsApr 06 2019The techniques of deep learning have become the state of the art methodology for executing complicated tasks from various domains of computer vision, natural language processing, and several other areas. Due to its rapid development and promising benchmarks ... More

A Novel Continuous Representation of Genetic Programmings using Recurrent Neural Networks for Symbolic RegressionApr 06 2019Neuro-encoded expression programming that aims to offer a novel continuous representation of combinatorial encoding for genetic programming methods is proposed in this paper. Genetic programming with linear representation uses nature-inspired operators ... More

Reducing catastrophic forgetting when evolving neural networksApr 05 2019A key stepping stone in the development of an artificial general intelligence (a machine that can perform any task), is the production of agents that can perform multiple tasks at once instead of just one. Unfortunately, canonical methods are very prone ... More

Data-driven Modelling of Dynamical Systems Using Tree Adjoining Grammar and Genetic ProgrammingApr 05 2019State-of-the-art methods for data-driven modelling of non-linear dynamical systems typically involve interactions with an expert user. In order to partially automate the process of modelling physical systems from data, many EA-based approaches have been ... More

Learning to Remember: A Synaptic Plasticity Driven Framework for Continual LearningApr 05 2019Models trained in the context of continual learning (CL) should be able to learn from a stream of data over an undefined period of time. The main challenges herein are: 1) maintaining old knowledge while simultaneously benefiting from it when learning ... More

An Evolutionary Framework for Automatic and Guided Discovery of AlgorithmsApr 05 2019This paper presents Automatic Algorithm Discoverer (AAD), an evolutionary framework for synthesizing programs of high complexity. To guide evolution, prior evolutionary algorithms have depended on fitness (objective) functions, which are challenging to ... More

Fluxonic Processing of Photonic Synapse EventsApr 04 2019Much of the information processing performed by a neuron occurs in the dendritic tree. For neural systems using light for communication, it is advantageous to convert signals to the electronic domain at synaptic terminals so dendritic computation can ... More

Learning Numeracy: Binary Arithmetic with Neural Turing MachinesApr 04 2019One of the main problems encountered so far with recurrent neural networks is that they struggle to retain long-time information dependencies in their recurrent connections. Neural Turing Machines (NTMs) attempt to mitigate this issue by providing the ... More

Self-Adapting Goals Allow Transfer of Predictive Models to New TasksApr 04 2019A long-standing challenge in Reinforcement Learning is enabling agents to learn a model of their environment which can be transferred to solve other problems in a world with the same underlying rules. One reason this is difficult is the challenge of learning ... More

Convergence analysis of beetle antennae search algorithm and its applicationsApr 04 2019The beetle antennae search algorithm was recently proposed and investigated for solving global optimization problems. Although the performance of the algorithm and its variants were shown to be better than some existing meta-heuristic algorithms, there ... More

Preference Neural NetworkApr 04 2019This paper proposes a preference neural network (PNN) to address the problem of indifference preferences orders with new activation function. PNN also solves the Multi-label ranking problem, where labels may have indifference preference orders or subgroups ... More

Consistency by Agreement in Zero-shot Neural Machine TranslationApr 04 2019Generalization and reliability of multilingual translation often highly depend on the amount of available parallel data for each language pair of interest. In this paper, we focus on zero-shot generalization---a challenging setup that tests models on ... More

Consistency by Agreement in Zero-shot Neural Machine TranslationApr 04 2019Apr 10 2019Generalization and reliability of multilingual translation often highly depend on the amount of available parallel data for each language pair of interest. In this paper, we focus on zero-shot generalization---a challenging setup that tests models on ... More

Model-based Genetic Programming with GOMEA for Symbolic Regression of Small ExpressionsApr 03 2019The Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA) has been shown to be a top performing EA in several domains, including Genetic Programming (GP). Differently from traditional EAs where variation acts randomly, GOMEA learns a model of interdependencies ... More

A Model-based Genetic Programming Approach for Symbolic Regression of Small ExpressionsApr 03 2019Apr 05 2019The Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA) is a model-based EA framework that has been shown to perform well in several domains, including Genetic Programming (GP). Differently from traditional EAs where variation acts randomly, GOMEA ... More

Extracting Tables from Documents using Conditional Generative Adversarial Networks and Genetic AlgorithmsApr 03 2019Extracting information from tables in documents presents a significant challenge in many industries and in academic research. Existing methods which take a bottom-up approach of integrating lines into cells and rows or columns neglect the available prior ... More

Multi-layered Spiking Neural Network with Target Timestamp Threshold Adaptation and STDPApr 03 2019Spiking neural networks (SNNs) are good candidates to produce ultra-energy-efficient hardware. However, the performance of these models is currently behind traditional methods. Introducing multi-layered SNNs is a promising way to reduce this gap. We propose ... More

Testing Self-Organizing Multiagent SystemsApr 03 2019Multiagent Systems (MASs) involve different characteristics, such as autonomy, asynchronous and social features, which make these systems more difficult to understand. Thus, there is a lack of procedures guaranteeing that multiagent systems would behave ... More

Evolving Plasticity for Autonomous Learning under Changing Environmental ConditionsApr 02 2019A fundamental aspect of learning in biological neural networks (BNNs) is the plasticity property which allows them to modify their configurations during their lifetime. Hebbian learning is a biologically plausible mechanism for modeling the plasticity ... More

Improving Noise Tolerance of Mixed-Signal Neural NetworksApr 02 2019Mixed-signal hardware accelerators for deep learning achieve orders of magnitude better power efficiency than their digital counterparts. In the ultra-low power consumption regime, limited signal precision inherent to analog computation becomes a challenge. ... More

Optimising Trotter-Suzuki Decompositions for Quantum Simulation Using Evolutionary StrategiesApr 02 2019One of the most promising applications of near-term quantum computing is the simulation of quantum systems, a classically intractable task. Quantum simulation requires computationally expensive matrix exponentiation; Trotter-Suzuki decomposition of this ... More

Optimising Trotter-Suzuki Decompositions for Quantum Simulation Using Evolutionary StrategiesApr 02 2019Apr 15 2019One of the most promising applications of near-term quantum computing is the simulation of quantum systems, a classically intractable task. Quantum simulation requires computationally expensive matrix exponentiation; Trotter-Suzuki decomposition of this ... More

Multimodal Sparse Classifier for Adolescent Brain Age PredictionApr 01 2019The study of healthy brain development helps to better understand the brain transformation and brain connectivity patterns which happen during childhood to adulthood. This study presents a sparse machine learning solution across whole-brain functional ... More

Invariance-Preserving Localized Activation Functions for Graph Neural NetworksMar 29 2019Graph signals are signals with an irregular structure that can be described by a graph. Graph neural networks (GNNs) are information processing architectures tailored to these graph signals and made of stacked layers that compose graph convolutional filters ... More

An Upper Bound for Minimum True Matches in Graph Isomorphism with Simulated AnnealingMar 29 2019Graph matching is one of the most important problems in graph theory and combinatorial optimization, with many applications in various domains. Although meta-heuristic algorithms have had good performance on many NP-Hard and NP-Complete problems, for ... More

Using Structured Input and Modularity for Improved LearningMar 29 2019We describe a method for utilizing the known structure of input data to make learning more efficient. Our work is in the domain of programming languages, and we use deep neural networks to do program analysis. Computer programs include a lot of structural ... More

Neuromorphic In-Memory Computing Framework using Memtransistor Cross-bar based Support Vector MachinesMar 29 2019This paper presents a novel framework for designing support vector machines (SVMs), which does not impose restriction on the SVM kernel to be positive-definite and allows the user to define memory constraint in terms of fixed template vectors. This makes ... More

Deep Convolutional Spiking Neural Networks for Image ClassificationMar 28 2019Spiking neural networks are biologically plausible counterparts of the artificial neural networks, artificial neural networks are usually trained with stochastic gradient descent and spiking neural networks are trained with spike timing dependant plasticity. ... More

Evolving Boolean Functions with Conjunctions and Disjunctions via Genetic ProgrammingMar 28 2019Recently it has been proved that simple GP systems can efficiently evolve the conjunction of $n$ variables if they are equipped with the minimal required components. In this paper, we make a considerable step forward by analysing the behaviour and performance ... More

A Multi Hidden Recurrent Neural Network with a Modified Grey Wolf OptimizerMar 27 2019Identifying university students' weaknesses results in better learning and can function as an early warning system to enable students to improve. However, the satisfaction level of existing systems is not promising. New and dynamic hybrid systems are ... More

Echo State Networks with Self-Normalizing Activations on the Hyper-SphereMar 27 2019Among the various architectures of Recurrent Neural Networks, Echo State Networks (ESNs) emerged due to their simplified and inexpensive training procedure. These networks are known to be sensitive to the setting of hyper-parameters, which critically ... More

On Inversely Proportional Hypermutations with Mutation PotentialMar 27 2019Artificial Immune Systems (AIS) employing hypermutations with linear static mutation potential have recently been shown to be very effective at escaping local optima of combinatorial optimisation problems at the expense of being slower during the exploitation ... More

Self-adaptive decision-making mechanisms to balance the execution of multiple tasks for a multi-robots teamMar 27 2019This work addresses the coordination problem of multiple robots with the goal of finding specific hazardous targets in an unknown area and dealing with them cooperatively. The desired behaviour for the robotic system entails multiple requirements, which ... More

Symbolic Regression for Constructing Analytic Models in Reinforcement LearningMar 27 2019Reinforcement learning (RL) is a widely used approach for controlling systems with unknown or time-varying dynamics. Even though RL does not require a model of the system, it is known to be faster and safer when using models learned online. We propose ... More

Multi-Species Cuckoo Search Algorithm for Global OptimizationMar 27 2019Many optimization problems in science and engineering are highly nonlinear, and thus require sophisticated optimization techniques to solve. Traditional techniques such as gradient-based algorithms are mostly local search methods, and often struggle to ... More

Spontaneous Facial Micro-Expression Recognition using 3D Spatiotemporal Convolutional Neural NetworksMar 27 2019Facial expression recognition in videos is an active area of research in computer vision. However, fake facial expressions are difficult to be recognized even by humans. On the other hand, facial micro-expressions generally represent the actual emotion ... More

A Simple Haploid-Diploid Evolutionary AlgorithmMar 27 2019It has recently been suggested that evolution exploits a form of fitness landscape smoothing within eukaryotic sex due to the haploid-diploid cycle. This short paper presents a simple modification to the standard evolutionary computing algorithm to similarly ... More

Scaling up the randomized gradient-free adversarial attack reveals overestimation of robustness using established attacksMar 27 2019Modern neural networks are highly non-robust against adversarial manipulation. A significant amount of work has been invested in techniques to compute lower bounds on robustness through formal guarantees and to build provably robust model. However it ... More

A novel framework for automatic detection of Autism: A study on Corpus Callosum and Intracranial Brain VolumeMar 27 2019Computer vision and machine learning are the linchpin of field of automation. The medicine industry has adopted numerous methods to discover the root causes of many diseases in order to automate detection process. But, the biomarkers of Autism Spectrum ... More

Improved robustness of reinforcement learning policies upon conversion to spiking neuronal network platforms applied to ATARI gamesMar 26 2019Various implementations of Deep Reinforcement Learning (RL) demonstrated excellent performance on tasks that can be solved by trained policy, but they are not without drawbacks. Deep RL suffers from high sensitivity to noisy and missing input and adversarial ... More