The university timetabling, examination and course, are known to be highly constrained optimization problems. Metaheuristic approaches, and their hybrids, have successfully been applied to solve the problems. This paper presents three artificial immune algorithms, the algorithms inspired by the immune system, for university timetabling; clonal selection, immune network and negative selection. T...

Argumentation is a vital aspect of intelligent behaviour by humans. It provides the means for comparing information by analysing pros and cons when trying to make a decision. Formalising argumentation in computational environment has become a topic of increasing interest in artificial intelligence research over the last decade. Computational argumentation involves reasoning with uncertainty by ...

Genetic Algorithms (GAs) are a stochastic searching and optimizing method inspired by the biological mechanism of natural selection and evolution. To improve the searching power of GAs for complicated problems, many deterministic measures, particularly, experience and/or expert knowledge-based heuristic rules, have been studied in the existing literature. This paper proposes a potentially more ...

In this study, three major pruning techniques are used to examine the effects of network pruning on classification accuracy. A feed-forward neural network structure which learns the characteristics of the training data via the backpropagation learning algorithm is employed to classify a multisource remotely sensed data set. An important conclusion from this study is that, of the three technique...

We employ a virtual marine world inhabited by realistic artificial animals as an ALife laboratory for developing and evaluating zoomimetic perception and learning algorithms. In particular, we propose active perception strategies that enable artificial marine animals to navigate purposefully through their world by using computer vision algorithms to analyze the foveated retinal image streams ac...

We are trying to piece together the knowledge of evolution with the help of biology, informatics and physics to create complex evolutionary algorithms with parallel and hierarchical structure. It can speed up the creation of optimization algorithms with high quality features. The adaptive significance of genetic algorithms (GAs) with diploid chromosomes and an artificial immune system has been ...

The objective of this research was to determine the best model and compare performances in terms of producing landuse maps from six supervised classification algorithms. As a result, different algorithms such as the minimum distance ofmean (MDM), Mahalanobis distance (MD), maximum likelihood (ML), artificial neural network (ANN), spectral anglemapper (SAM), and support vector machine (SVM) were...

In the last decade, high profile financial frauds committed by large companies in both developed and developing countries were discovered and reported. This study compares the performance of five popular statistical and machine learning models in detecting financial statement fraud. The research objects are companies which experienced both fraudulent and non-fraudulent financial statements betw...

Artificial neural networks as a major soft-computing technology have been extensively studied and applied during the last three decades. Research on backpropagation training algorithms for multilayer perceptron networks has spurred development of other neural network training algorithms for other networks such as radial basis function, recurrent network, feedback network, and unsupervised Kohon...

Deep learning is one of the subsets of machine learning that is widely used in Artificial Intelligence (AI) field such as natural language processing and machine vision. The learning algorithms require optimization in multiple aspects. Generally, model-based inferences need to solve an optimized problem. In deep learning, the most important problem that can be solved by optimization is neural n...