A note on the variance of rank-based selection strategies for genetic algorithms and genetic programming

This paper evaluates different forms of rank-based selection that are used with genetic algorithms and genetic programming. Many types of rank based selection have exactly the same expected value in terms of the sampling rate allocated to each member of the population. However, the variance associated with that sampling rate can vary depending on how selection is implemented. We examine two forms of tournament selection and compare these to linear rank-based selection using an explicit formula. Because selective pressure has a direct impact on population diversity, we also examine the interaction between selective pressure and different mutation strategies.     

Genetic programming of conventional features to detect seizure precursors

This paper presents an application of genetic programming (GP) to optimally select and fuse conventional features (C-features) for the detection of epileptic waveforms within intracranial electroencephalogram (IEEG) recordings that precede seizures, known as seizure precursors. Evidence suggests that seizure precursors may localize regions important to seizure generation on the IEEG and epilepsy treatment. However, current methods to detect epileptic precursors lack a sound approach to automatically select and combine C-features that best distinguish epileptic events from background, relying on visual review predominantly. This work suggests GP as an optimal alternative to create a single feature after evaluating the performance of a binary detector that uses: (1) genetically programmed features; (2) features selected via GP; (3) forward sequentially selected features; and (4) visually selected features. Results demonstrate that a detector with a genetically programmed feature outperforms the other three approaches, achieving over 78.5% positive predictive value, 83.5% sensitivity, and 93% specificity at the 95% level of confidence.     

The Lambda Library: unnamed functions in C++

The Lambda Library (LL) adds a form of lambda functions to C++, which are common in functional programming languages. The LL is implemented as a template library using standard C++; thus no language extensions or preprocessing is required. The LL consists of a rich set of tools for defining unnamed functions. In particular these unnamed functions work seamlessly with the generic algorithms in the C++ Standard Library. The LL offers significant improvements, in terms of generality and ease of use, compared to the current tools in the C++ Standard Library. Copyright © 2003 John Wiley & Sons, Ltd.     

Metric-based reasoning about pseudocode design in the partial metrics system

The goal of the PMS project is to produce an environment in which the intelligent online assessment of the design for large-scale ADA programming projects is provided. The focus is on the representation of knowledge about the design process for an individual module. Changes in pseudocode complexity are measured in terms of partial metrics. These metrics can take the designers inferences about the pseudocode program structure into account when assessing module complexity. Next, a model of the stepwise refinement process is given which demonstrates how pseudocode elaboration decisions can be modelled in partial metric terms. Finally, the decisions associated with each refinement step for 17 example refinements taken from the computer science literature are described using partial metrics.     

Fuzzy Multiobjective and Linear Programming Based Management Models for Optimal Land-Water-Crop System Planning

In this article, linear programming and fuzzy optimization models are developed for planning and management of available land-water-crop system of Mahanadi-Kathajodi delta in eastern India. The models are used to optimize the economic return, production and labour utilization, and to search the related cropping patterns and intensities with specified land, water, fertilizer and labour availability, and water use pattern constraints. Due to extreme backwardness of the study area, it has been decided to keep all the three objectives of the linear programming models at the same priority level to obtain the compromised solution in a fuzzy environment that incorporates the imprecision in fuzzy goals and fuzzy constraints. These non-structural models facilitate the conjunctive use of available surface water and groundwater resources. A comparative evaluation along with the benefit-cost ratios of the existing and proposed farming systems is also presented.     

A machine learning system for identifying transmembrane domains from amino acid sequences

We present our machine learning system, that uses inductive logic programming techniques to learn how to identify transmembrane domains from amino acid sequences. Our system facilitates the use of operators such as ‘contains’, that act on entire sequences, rather than on individual elements of a sequence. The prediction accuracy of our new system is around 93%, and this compares favourably with earlier results. This work was carried out with the support of a research grant from ISIS, Fujitsu Laboratories.     

Sub-optimal solutions to track detection problem using graph theoretic concepts

This paper demonstrates how the problem of tracking targets, which appear as either straight or curved lines in two-dimensional display images (or data images) can be formulated in terms of a directed weighted graph model and how dynamic programming techniques can be efficiently applied to reach an optimal or sub-optimal solution. In general, track detection algorithms providing optimal solutions have good detective ability, but most of them suffer from the inability to detect discontinuous lines or to resolve efficiently pairs of crossing lines. A sub-optimal solution is provided that efficiently overcomes these weaknesses. We focus on modeling the track detection problem in terms of a graph, formulating fast sequential/parallel sub-optimal track detection algorithms and testing them on simulated data in order to show their detective ability. Moreover, we specify the conditions under which sub-optimal algorithms can perform at least as well as their corresponding optimal algorithms. This is significant for the track detection problem where fast, accurate and real-time detection is considered a necessity.     

The pickup and delivery problem: Faces and branch-and-cut algorithm

This paper formulates the pickup and delivery problem, also known as the dial-a-ride problem, as an integer program. Its polyhedral structure is explored and four classes of valid inequalities developed. The results of a branch-and-cut algorithm based on these constraints are presented.     

A Neural Network Model of Caenorhabditis Elegans: The Circuit of Touch Sensitivity

The paper presents a neural network model of the touch sensitivity circuit of the nematode Caenorhabditis elegans. We describe a serie of simulations in which neural networks are trained, using a genetic algorithm, to reproduce the habituation of the nematode's touch sensitive behavior. A lesion study of the network allows to make a direct comparison between the fine functioning of the model and the data collected in real organisms. The model accords well with the known neurobiological data and it suggests some hypotheses about the functioning of the neural circuit and of single neurons.