Kok Wai Wong

Classification of adaptive memetic algorithms: a comparative study

Adaptation of parameters and operators represents one of the recent most important and promising areas of research in evolutionary computations; it is a form of designing self-configuring algorithms that acclimatize to suit the problem in hand. Here, our interests are on a recent breed of hybrid evolutionary algorithms typically known as adaptive memetic algorithms (MAs). One unique feature of adaptive MAs is the choice of local search methods or memes and recent studies have shown that this choice significantly affects the performances of problem searches. In this paper, we present a classification of memes adaptation in adaptive MAs on the basis of the mechanism used and the level of historical knowledge on the memes employed. Then the asymptotic convergence properties of the adaptive MAs considered are analyzed according to the classification. Subsequently, empirical studies on representatives of adaptive MAs for different type-level meme adaptations using continuous benchmark problems indicate that global-level adaptive MAs exhibit better search performances. Finally we conclude with some promising research directions in the area.

Fuzzy rule interpolation for multidimensional input spaces with applications: a case study

Fuzzy rule based systems have been very popular in many engineering applications. However, when generating fuzzy rules from the available information, this may result in a sparse fuzzy rule base. Fuzzy rule interpolation techniques have been established to solve the problems encountered in processing sparse fuzzy rule bases. In most engineering applications, the use of more than one input variable is common, however, the majority of the fuzzy rule interpolation techniques only present detailed analysis to one input variable case. This paper investigates characteristics of two selected fuzzy rule interpolation techniques for multidimensional input spaces and proposes an improved fuzzy rule interpolation technique to handle multidimensional input spaces. The three methods are compared by means of application examples in the field of petroleum engineering and mineral processing. The results show that the proposed fuzzy rule interpolation technique for multidimensional input spaces can be used in engineering applications.

Surrogate-assisted evolutionary optimization frameworks for high-fidelity engineering design problems

Over the last decade, Evolutionary Algorithms (EAs) have emerged as a powerful paradigm for global optimization of multimodal functions. More recently, there has been significant interest in applying EAs to engineering design problems. However, in many complex engineering design problems where high-fidelity analysis models are used, each function evaluation may require a Computational Structural Mechanics (CSM), Computational Fluid Dynamics (CFD) or Computational Electro-Magnetics (CEM) simulation costing minutes to hours of supercomputer time. Since EAs typically require thousands of function evaluations to locate a near optimal solution, the use of EAs often becomes computationally prohibitive for this class of problems. In this chapter, we present frameworks that employ surrogate models for solving computationally expensive optimization problems on a limited computational budget. In particular, the key factors responsible for the success of these frameworks are discussed. Experimental results obtained on benchmark test functions and real-world complex design problems are presented.

How does desktop virtual reality enhance learning outcomes? A structural equation modeling approach

This study examined how desktop virtual reality (VR) enhances learning and not merely does desktop VR influence learning. Various relevant constructs and their measurement factors were identified to examine how desktop VR enhances learning and the fit of the hypothesized model was analyzed using structural equation modeling. The results supported the indirect effect of VR features to the learning outcomes, which was mediated by the interaction experience and the learning experience. Learning experience which was individually measured by the psychological factors, that is, presence, motivation, cognitive benefits, control and active learning, and reflective thinking took central stage in affecting the learning outcomes in the desktop VR-based learning environment. The moderating effect of student characteristics such as spatial ability and learning style was also examined. The results show instructional designers and VR software developers how to improve the learning effectiveness and further strengthen their desktop VR-based learning implementation. Through this research, an initial theoretical model of the determinants of learning effectiveness in a desktop VR-based learning environment is contributed.

Fuzzy Rule Interpolation Matlab Toolbox - FRI Toolbox

In most fuzzy systems, the completeness of the fuzzy rule base is required to generate meaningful output when classical fuzzy reasoning methods are applied. This means, in other words, that the fuzzy rule base has to cover all possible inputs. Regardless of the way of rule base construction, be it created by human experts or by an automated manner, often incomplete rule bases are generated. One simple solution to handle sparse fuzzy rule bases and to make infer reasonable output is the application of fuzzy rule interpolation (FRI) methods. In this paper, we present a Fuzzy Rule Interpolation Matlab Toolbox, which is freely available. With the introduction of this Matlab Toolbox, different FRI methods can be used for different real time applications, which have sparse or incomplete fuzzy rule base.

Distinguishing games and simulation games from simulators

The advanced computational capabilities in modern personal computers have made it possible for consumers to experience simulations with a high degree of verisimilitude through simulation games (a.k.a. Sims). In recent years, the cross-boundary technology exchange between game and simulation technology, along with other factors, has contributed to the confusion as to what makes a simulation game and what makes a simulator. This article provides a users and designers perspective on a definitive comparison of the similarities and differences between games in general, simulation games, and simulators. It also introduces a method that can be easily used to distinguish games and simulation games from simulators by using observable design characteristics. On the other hand, the convergence of functionality and technology in simulation games and simulators has created new applications of simulation. One such application is in serious games. Serious games and simulation games are confusingly similar in many ways. However, they greatly differ in functionality. This article also provides a method to distinguish serious games from simulation games, to clarify the strict categorization between these two applications of simulation.

Comparing the performance of different neural networks for binary classification problems

Classification problem is a decision making task where many researchers have been working on. There are a number of techniques proposed to perform classification. Neural network is one of the artificial intelligent techniques that has many successful examples when applying to this problem. This paper presents a comparison of neural network techniques for binary classification problems. The classification performance obtained by five different types of neural networks for comparison are Back Propagation Neural Network (BPNN), Radial Basis Function Neural Network (RBFNN), General Regression Neural Network (GRNN), Probabilistic Neural Network (PNN), and Complementary Neural Network (CMTNN). The comparison is done based on three benchmark data sets obtained from UCI machine learning repository. The results show that CMTNN typically provide better classification results when comparing to techniques applied to binary classification problems.

Modular artificial neural network for prediction of petrophysical properties from well log data

This paper reports the application of Kohonens Self-Organizing Map (SOM) and Learning Vector Quantization (LVQ) algorithms, and the commonly used Back Propagation Neural Network (BPNN) to the prediction of petrophysical properties from well log data. Recently, the use of artificial neural networks (ANN) in the field of petrophysical properties prediction has received increasing attentions. In this paper, a modular ANN comprises of a complex network made up of a number of sub-networks is introduced. In this approach, the SOM algorithm is first applied to classify the well log data into a pre-defined number of classes. This gives an indication of the lithology of the given well. The LVQ algorithm is then applied to train the network under supervised learning. A set of BPNN which corresponds to different classes is then developed for the prediction of petrophysical properties. Once the network is trained if is then used as the classification and prediction model for subsequent input data. Results obtained from example studies using this proposed method have shown to be fast and accurate as compared to a single BPNN network.

Classification of imbalanced data by combining the complementary neural network and SMOTE algorithm

In classification, when the distribution of the training data among classes is uneven, the learning algorithm is generally dominated by the feature of the majority classes. The features in the minority classes are normally difficult to be fully recognized. In this paper, a method is proposed to enhance the classification accuracy for the minority classes. The proposed method combines Synthetic Minority Over-sampling Technique (SMOTE) and Complementary Neural Network (CMTNN) to handle the problem of classifying imbalanced data. In order to demonstrate that the proposed technique can assist classification of imbalanced data, several classification algorithms have been used. They are Artificial Neural Network (ANN), k-Nearest Neighbor (k-NN) and Support Vector Machine (SVM). The benchmark data sets with various ratios between the minority class and the majority class are obtained from the University of California Irvine (UCI) machine learning repository. The results show that the proposed combination techniques can improve the performance for the class imbalance problem.

Rainfall prediction model using soft computing technique

Abstract Rainfall prediction in this paper is a spatial interpolation problem that makes use of the daily rainfall information to predict volume of rainfall at unknown locations within area covered by existing observations. This paper proposed the use of self-organising map (SOM), backpropagation neural networks (BPNN) and fuzzy rule systems to perform rainfall spatial interpolation based on local method. The SOM is first used to separate the whole data space into some local surface automatically without any knowledge from the analyst. In each sub-surface, the complexity of the whole data space is reduced to something more homogeneous. After classification, BPNNs are then use to learn the generalization characteristics from the data within each cluster. Fuzzy rules for each cluster are then extracted. The fuzzy rule base is then used for rainfall prediction. This method is used to compare with an established method, which uses radial basis function networks and orographic effect. Results show that this method could provide similar results from the established method. However, this method has the advantage of allowing analyst to understand and interact with the model using fuzzy rules.