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Moving object tracking in video sequences is an important task in the field of computer vision. In this paper, we propose a new population-based algorithm namely simplified swarm optimization (SSO) for tracking arbitrary objects. In SSO, the object model is first projected into a high-dimensional feature space, then the particles will fly over image pixels to find an optimal match of the target. While...
This paper presents a Genetic Algorithm (GA) based evolution framework in which Spiking Neural Network (SNN) of single or a colony of artificial creatures are evolved for higher chance of survival in a virtual environment. The artificial creatures are composed of randomly connected Izhikevich spiking reservoir neural networks. Inspired by biological neurons, the neuronal connections are considered...
This paper presents a biologically plausible method for converting real-valued input into spike trains for processing with spiking neural networks. The proposed method mimics the adaptive behaviour of retinal ganglion cells and allows input neurons to adapt their response to changes in the statistics of the input. Thus, rather than passively receiving values and forwarding them to the hidden and output...
Following analyzing existing challenges in addressing the balance between exploration and exploitation encountered by evolutionary algorithms, this paper develops a Genetic Algorithm with speciation (GASP). It first incorporates a novel encoding scheme and recombination method for a balanced genetic divergence when locating global optima in complex applications, such as structural and dynamic design...
Many processes in nature display quasi-periodic behavior, including variable stars in distant galaxies and oscillations in brains. In this work we model quasi-periodic lightcurves using neuropercolation, which describes complex spatio-temporal oscillations arising from random cellular automata near criticality. We show that neuropercolation is able to model lightcurves from various stars of the gamma-Doradus...
The application of genetic programming (GP) to streaming data analysis appears, on the face of it, to be a less than obvious choice. If nothing else, the (perceived) computational cost of model building under GP would preclude its application to tasks with non-stationary properties. Conversely, there is a rich history of applying GP to various tasks associated with trading agent design for currency...
Most improvements for Naive Bayes (NB) have a common yet important flaw - these algorithms split the modeling of the classifier into two separate stages - the stage of preprocessing (e.g., feature selection and data expansion) and the stage of building the NB classifier. The first stage does not take the NB's objective function into consideration, so the performance of the classification cannot be...
The cooperative co-evolution framework (CC) is widely used in the large scale global optimization. It is believed that the CC framework is very sensitive to grouping strategies and the performance deteriorate if interacted variables are not correctly grouped. So many efforts have been devoted to find good ways to correctly decompose the large scale problem into smaller sub-problems so as to effectively...
Evolutionary meta-heuristics are designed for optimization using population with selection and mutation operators. Novelty of our approach is based on competition of various operators from mutation portfolio. Resulting meta-heuristic is successfully tested on the feature selection task: searching for a sparse sub-model having the best possible value by means of information criteria. Beginning with...
An adaptive target scheme is implemented for learning control of population transfer between subspaces of quantum systems. In this control scheme, the target state is updated according to the renormalized yield in the desired subspace throughout the learning iterations, to obtain the desired laser control field. In the numerical experiments, we perform learning control simulations based on a V-type...
This paper proposes two constraint-handling techniques based on multiobjective optimization with biased dynamic weights for constrained optimization problems (COPs). Transforming a COP into an unconstrained biobjective optimization, two popular strategies based on decomposition, i.e. Tchebycheff approach (TEA) and weighted sum approach (WSA) are used in this paper respectively. In order to keep a...
Recent digital spiking neuromorphic chips can perform complex computations in real-time with very low power consumption. The input data to such systems needs to first be converted into spikes using a spike encoding scheme. Current examples of such schemes include rate codes and population codes. The selected coding scheme might heavily impact the system's energy consumption, communication bandwidth,...
Many real-world problems are usually unbalanced, where datasets present skewed class distributions, such as failure diagnosis, spam detection, anomaly detection, fraud detection, oil spillage detection and medical diagnosis, etc. Deep Belief Network (DBN) is a competitive machine learning technique with good performance in many applications. However, some machine learning methods are likely to give...
We introduce a technique to compute probably approximately correct (PAC) bounds on precision and recall for matching algorithms. The bounds require some verified matches, but those matches may be used to develop the algorithms. The bounds can be applied to network reconciliation or entity resolution algorithms, which identify nodes in different networks or values in a data set that correspond to the...
Multi-Objective Evolutionary Algorithms (MOEAs) and transport simulators have been widely utilized to optimise traffic signal timings with multiple objectives. However, traffic simulations require much processing time and need to be called repeatedly in iterations of MOEAs. As a result, traffic signal timing optimisation process is time-consuming. Anytime behaviour of an algorithm indicates its ability...
Evolution is extremely creative. The mere availability of a mechanism for synaptic change seems to be enough for evolution to derive a learning rule. Many simulations of evolution have evolved learning in a highly guided manner. Either by constraining the update function to a Hebbian form, or by supplying an error/teaching signal. In this paper, we aim to evolve a more general learning rule. And since...
In this paper we propose a hardware implementation of a new deep brain stimulator that is able to desynchronize an abnormally synchronized neural population model. The proposed stimulator is based on a delay feedback technique and is applied on a population of neurons described with Izhikevich model. The whole structure of the stimulator and the neural population model are first simulated in software...
The biological brain is a highly plastic system within which the efficacy and structure of synaptic connections are constantly changing in response to internal and external stimuli. While numerous models of this plastic behavior exist at various levels of abstraction, how these mechanisms allow the brain to learn meaningful values is unclear. The Neural Engineering Framework (NEF) is a hypothesis...
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