Comparison of search algorithms in two-stage neural network training for optical character recognition of handwritten digits

Neural networks are a powerful machine learning technique that give a system the ability to develop multiple levels of abstraction to interpret data. Such networks require training to develop the neural layers and neuron weights in order to form reasonable conclusions from the data being interpreted. The conventional training method is to use backpropagation to feed the error between a neural network’s actual output and desired output back through the neural network to adjust the neural synapses’ weights to minimize such errors on subsequent training data. However, backpropagation has several limitations to its effectiveness in training neural networks. The most relevant limitation of backpropagation is that it tends to become trapped in local optimum solutions. This research studied the effectiveness in using search algorithms to improve upon a feed-forward Multi-Layer Perceptron artificial neural network trained by backpropagation in classifying handwritten digits. The search algorithms used for this research were the Bare Bones Fireworks Algorithm (BBFWA), Canonical Particle Swarm Optimization (PSO), and Cooperative Particle Swarm Optimization (CPSO) algorithms. Two sets of parameters for the BBFWA were tested in this study in order to examine the effects of parameters on the algorithm. The handwritten digit classification data was carried out on the MNIST handwritten character database, a common benchmark for handwritten character recognition. A neural network was trained with backpropagation, and then the search algorithms were seeded with its weights so that they could search for better neural network weight configurations. The complexity of using images of handwritten characters with a feed-forward Multi-Layer Perceptron resulted in a high degree of dimensionality in the problem, which negatively impacted the Particle Swarm Optimization algorithms. To analyze the impact of the problem dimensionality, the neural network was also tested with a PCA compressed MNIST database. It was found that the BBFWA performed the best out of the three algorithms on both datasets, as it was able to consistently improve upon the performance of the original neural networks. Between the two sets of BBFWA parameters, the simulation results indicated that the second parameter set outperformed the first parameter set in terms of both classification accuracy and fitness trends

A Lite Fireworks Algorithm with Fractal Dimension Constraint for Feature Selection

As the use of robotics becomes more widespread, the huge amount of vision data leads to a dramatic increase in data dimensionality. Although deep learning methods can effectively process these high-dimensional vision data. Due to the limitation of computational resources, some special scenarios still rely on traditional machine learning methods. However, these high-dimensional visual data lead to great challenges for traditional machine learning methods. Therefore, we propose a Lite Fireworks Algorithm with Fractal Dimension constraint for feature selection (LFWA+FD) and use it to solve the feature selection problem driven by robot vision. The "LFWA+FD" focuses on searching the ideal feature subset by simplifying the fireworks algorithm and constraining the dimensionality of selected features by fractal dimensionality, which in turn reduces the approximate features and reduces the noise in the original data to improve the accuracy of the model. The comparative experimental results of two publicly available datasets from UCI show that the proposed method can effectively select a subset of features useful for model inference and remove a large amount of noise noise present in the original data to improve the performance.Comment: International Conference on Pharmaceutical Sciences 202

The Hanabi Challenge: A New Frontier for AI Research

From the early days of computing, games have been important testbeds for studying how well machines can do sophisticated decision making. In recent years, machine learning has made dramatic advances with artificial agents reaching superhuman performance in challenge domains like Go, Atari, and some variants of poker. As with their predecessors of chess, checkers, and backgammon, these game domains have driven research by providing sophisticated yet well-defined challenges for artificial intelligence practitioners. We continue this tradition by proposing the game of Hanabi as a new challenge domain with novel problems that arise from its combination of purely cooperative gameplay with two to five players and imperfect information. In particular, we argue that Hanabi elevates reasoning about the beliefs and intentions of other agents to the foreground. We believe developing novel techniques for such theory of mind reasoning will not only be crucial for success in Hanabi, but also in broader collaborative efforts, especially those with human partners. To facilitate future research, we introduce the open-source Hanabi Learning Environment, propose an experimental framework for the research community to evaluate algorithmic advances, and assess the performance of current state-of-the-art techniques.Comment: 32 pages, 5 figures, In Press (Artificial Intelligence

Food emergency dispatching method based on optimized fireworks algorithm

In order to solve the problem of food emergency dispatching under emergencies, a food emergency dispatching method based on the optimal fireworks algorithm was proposed. The fitness function was used to measure the individual merits of fireworks, the tabu table was set to avoid the fireworks algorithm falling into the local optimal, and the tournament strategy was adopted as the iterative strategy of fireworks population. The goal of the fitness function is to maximize the satisfaction of demand points and minimize the vehicle travel time.In order to accurately predict the amount of food required at the point of demand, an infectious disease model (SEIR) was used.By comparing with the basic fireworks algorithm and genetic algorithm, the simulation results show that the proposed algorithm has higher computational efficiency and can be used in food emergency dispatching

A novel Fireworks Algorithm with wind inertia dynamics and its application to traffic forecasting

Fireworks Algorithm (FWA) is a recently contributed heuristic optimization method that has shown a promising performance in applications stemming from different domains. Improvements to the original algorithm have been designed and tested in the related literature. Nonetheless, in most of such previous works FWA has been tested with standard test functions, hence its performance when applied to real application cases has been scarcely assessed. In this manuscript a mechanism for accelerating the convergence of this meta-heuristic is proposed based on observed wind inertia dynamics (WID) among fireworks in practice. The resulting enhanced algorithm will be described algorithmically and evaluated in terms of convergence speed by means of test functions. As an additional novel contribution of this work FWA and FWA-WID are used in a practical application where such heuristics are used as wrappers for optimizing the parameters of a road traffic short-term predictive model. The exhaustive performance analysis of the FWA and FWA-ID in this practical setup has revealed that the relatively high computational complexity of this solver with respect to other heuristics makes it critical to speed up their convergence (specially in cases with a costly fitness evaluation as the one tackled in this work), observation that buttresses the utility of the proposed modifications to the naive FWA solver

On Identifying Disaster-Related Tweets: Matching-based or Learning-based?

Social media such as tweets are emerging as platforms contributing to situational awareness during disasters. Information shared on Twitter by both affected population (e.g., requesting assistance, warning) and those outside the impact zone (e.g., providing assistance) would help first responders, decision makers, and the public to understand the situation first-hand. Effective use of such information requires timely selection and analysis of tweets that are relevant to a particular disaster. Even though abundant tweets are promising as a data source, it is challenging to automatically identify relevant messages since tweet are short and unstructured, resulting to unsatisfactory classification performance of conventional learning-based approaches. Thus, we propose a simple yet effective algorithm to identify relevant messages based on matching keywords and hashtags, and provide a comparison between matching-based and learning-based approaches. To evaluate the two approaches, we put them into a framework specifically proposed for analyzing disaster-related tweets. Analysis results on eleven datasets with various disaster types show that our technique provides relevant tweets of higher quality and more interpretable results of sentiment analysis tasks when compared to learning approach