9804135
EPNet for Chaotic Time-Series Prediction
EPNet is an evolutionary system for automatic design of artificial neural networks (ANNs) [1, 2, 3]. Unlike most previous methods on evolving ANNs, EPNet puts its emphasis on evolving ANN'S behaviours rather than circuitry. The parsimony of evolved ANNs is encouraged by the sequential application of architectural mutations. In this paper, EP Net is applied to a couple of chaotic time-series prediction problems (i.e., the Mackey-Glass differential equation and the logistic map). The experimental results show that EPNet can produce very compact ANNs with good prediction ability in comparison with other algorithms.
37420469
Getting most out of evolutionary approaches
Evolutionary algorithms (EAs) have been widely used in evolvable hardware. The very term, evolvable hardware, reflects the importance and omnitude of EAs in this field. However, EAs have primarily been used as an optimisation or search tool, which can explore a large and complex space. While success has been demonstrated by EAs in exploring unconventional designs that are hard to reach by human experts, it is interesting to ask the question whether we have fully used all the potentialities of EAs. We argue in this paper that there is rich information in a population which can and should be exploited. The classical approach of evolving the best individual in a population may not be the best one. A truly population-based approach that emphasizes population rather than the best individual can often bring in several important benefits to evolvable hardware, including efficiency, accuracy, adaptiveness, and fault-tolerance.
1073181
Keeping Secrets within a Family: Rediscovering Parnas
David Parnas wrote several papers in the 1970’s and 1980’s that are now considered classics. The concepts he advocated such as information hiding and use of abstract interfaces are generally accepted as the appropriate way to design nontrivial software systems. However, not all of what he proposed has been fully appreciated and assimilated into our practices. Many of his simple, elegant ideas have been lost amongst the hype surrounding the technologies and methods that have arisen in the past two decades. This paper examines Parnas’s ideas, especially his emphasis on program families, and proposes that college-level computing science and software engineering curricula should renew their attention to these very important principles and techniques and present them in the context of contemporary software development.
206600425
Genetic programming-based decision trees for software quality classification
The knowledge of the likely problematic areas of a software system is very useful for improving its overall quality. Based on such information, a more focused software testing and inspection plan can be devised. Decision trees are attractive for a software quality classification problem which predicts the quality of program modules in terms of risk-based classes. They provide a comprehensible classification model which can be directly interpreted by observing the tree-structure. A simultaneous optimization of the classification accuracy and the size of the decision tree is a difficult problem, and very few studies have addressed the issue. This paper presents an automated and simplified genetic programming (gp) based decision tree modeling technique for the software quality classification problem. Genetic programming is ideally suited for problems that require optimization of multiple criteria. The proposed technique is based on multi-objective optimization using strongly typed GP. In the context of an industrial high-assurance software system, two fitness functions are used for the optimization problem: one for minimizing the average weighted cost of misclassification, and one for controlling the size of the decision tree. The classification performances of the GP-based decision trees are compared with those based on standard GP, i.e., S-expression tree. It is shown that the GP-based decision tree technique yielded better classification models. As compared to other decision tree-based methods, such as C4.5, GP-based decision trees are more flexible and can allow optimization of performance objectives other than accuracy. Moreover, it provides a practical solution for building models in the presence of conflicting objectives, which is commonly observed in software development practice.
15492987
Automatic Accurate Time-Bound Analysis for High-Level Languages
This paper describes a general approach for automatic and accurate time-bound analysis. The approach consists of transformations for building time-bound functions in the presence of partially known input structures, symbolic evaluation of the time-bound function based on input parameters, optimizations to make the overall analysis efficient as well as accurate, and measurements of primitive parameters, all at the source-language level. We have implemented this approach and performed a number of experiments for analyzing Scheme programs. The measured worst-case times are closely bounded by the calculated bounds.
10720584
Graph-Based Semi-Supervised Learning as a Generative Model
This paper proposes and develops a new graph-based semi-supervised learning method. Different from previous graph-based methods that are based on discriminative models, our method is essentially a generative model in that the class conditional probabilities are estimated by graph propagation and the class priors are estimated by linear regression. Experimental results on various datasets show that the proposed method is superior to existing graph-based semi-supervised learning methods, especially when the labeled subset alone proves insufficient to estimate meaningful class priors.
14257785
Comparison of probabilistic combination methods for protein secondary structure prediction
MOTIVATION
Protein secondary structure prediction is an important step towards understanding how proteins fold in three dimensions. Recent analysis by information theory indicates that the correlation between neighboring secondary structures are much stronger than that of neighboring amino acids. In this article, we focus on the combination problem for sequences, i.e. combining the scores or assignments from single or multiple prediction systems under the constraint of a whole sequence, as a target for improvement in protein secondary structure prediction.


RESULTS
We apply several graphical chain models to solve the combination problem and show that they are consistently more effective than the traditional window-based methods. In particular, conditional random fields (CRFs) moderately improve the predictions for helices and, more importantly, for beta sheets, which are the major bottleneck for protein secondary structure prediction.