Genetic algorithms in statistical tolerancing

An important aspect in manufacturing design is the distribution of geometrical tolerances so that an assembly functions with given probability, while minimising the manufacturing cost. This requires a complex search over a multidimensional domain, much of which leads to infeasible solutions and which can have many local minima. As well, Monte-Carlo methods are often required to determine the probability that the assembly functions as designed. This paper describes a genetic algorithm for carrying out this search and successfully applies it to two specific mechanical designs, enabling comparisons of a new statistical tolerancing design method with existing methods. (C) 2003 Elsevier Ltd. All rights reserved.

A review of the first year's experience with an automatic message-routing system for low-cost telemedicine

The Swinfen Charitable Trust has used email for some years as a low-cost telemedicine medium to provide consultant support for doctors in developing countries. A scalable, automatic message-routing system was constructed which automates many of the tasks involved in message handling. During the first 12 months of its use, 1510 messages were processed automatically. There were 128 referrals from 18 hospitals in nine countries. Of these 128 queries, 89 (70%) were replied to within 72 h; the median delay was 1.1 day. The 39 unanswered queries were sent to backup specialists for reply and 36 of them (92%) were replied to within 72 h. In the remaining three cases, a second-line (backup) specialist was required. The referrals were handled by 54 volunteer specialists from a panel of over 70. Two system operators, located 10 time zones apart, managed the system. The median time from receipt of a new referral to its allocation to a specialist was 0.2 days (interquartile range, IQR, 0.1-0.8). The median interval between receipt of a new referral and first reply was 2.6 days (IQR 0.8-5.9). Automatic message handling solves many of the problems of manual email telemedicine systems and represents a potentially scalable way of doing low-cost telemedicine in the developing world.

Genetic algorithms with applications in wireless communications

Genetic algorithms (GAs) are known to locate the global optimal solution provided sufficient population and/or generation is used. Practically, a near-optimal satisfactory result can be found by Gas with a limited number of generations. In wireless communications, the exhaustive searching approach is widely applied to many techniques, such as maximum likelihood decoding (MLD) and distance spectrum (DS) techniques. The complexity of the exhaustive searching approach in the MLD or the DS technique is exponential in the number of transmit antennas and the size of the signal constellation for the multiple-input multiple-output (MIMO) communication systems. If a large number of antennas and a large size of signal constellations, e.g. PSK and QAM, are employed in the MIMO systems, the exhaustive searching approach becomes impractical and time consuming. In this paper, the GAs are applied to the MLD and DS techniques to provide a near-optimal performance with a reduced computational complexity for the MIMO systems. Two different GA-based efficient searching approaches are proposed for the MLD and DS techniques, respectively. The first proposed approach is based on a GA with sharing function method, which is employed to locate the multiple solutions of the distance spectrum for the Space-time Trellis Coded Orthogonal Frequency Division Multiplexing (STTC-OFDM) systems. The second approach is the GA-based MLD that attempts to find the closest point to the transmitted signal. The proposed approach can return a satisfactory result with a good initial signal vector provided to the GA. Through simulation results, it is shown that the proposed GA-based efficient searching approaches can achieve near-optimal performance, but with a lower searching complexity comparing with the original MLD and DS techniques for the MIMO systems.

System for foreign exchange trading using genetic algorithms and reinforcement learning

Foreign exchange trading has emerged recently as a significant activity in many countries. As with most forms of trading, the activity is influenced by many random parameters so that the creation of a system that effectively emulates the trading process will be very helpful. A major issue for traders in the deregulated Foreign Exchange Market is when to sell and when to buy a particular currency in order to maximize profit. This paper presents novel trading strategies based on the machine learning methods of genetic algorithms and reinforcement learning.

Evaluation of cosmic ray rejection algorithms on single-shot exposures

To maximise data output from single-shot astronomical images, the rejection of cosmic rays is important. We present the results of a benchmark trial comparing various cosmic ray rejection algorithms. The procedures assess relative performances and characteristics of the processes in cosmic ray detection, rates of false detections of true objects, and the quality of image cleaning and reconstruction. The cosmic ray rejection algorithms developed by Rhoads (2000, PASP, 112, 703), van Dokkum (2001, PASP, 113, 1420), Pych (2004, PASP, 116, 148), and the IRAF task xzap by Dickinson are tested using both simulated and real data. It is found that detection efficiency is independent of the density of cosmic rays in an image, being more strongly affected by the density of real objects in the field. As expected, spurious detections and alterations to real data in the cleaning process are also significantly increased by high object densities. We find the Rhoads' linear filtering method to produce the best performance in the detection of cosmic ray events; however, the popular van Dokkum algorithm exhibits the highest overall performance in terms of detection and cleaning.

The correspondence framework for 3D surface matching algorithms

Beyond the inherent technical challenges, current research into the three dimensional surface correspondence problem is hampered by a lack of uniform terminology, an abundance of application specific algorithms, and the absence of a consistent model for comparing existing approaches and developing new ones. This paper addresses these challenges by presenting a framework for analysing, comparing, developing, and implementing surface correspondence algorithms. The framework uses five distinct stages to establish correspondence between surfaces. It is general, encompassing a wide variety of existing techniques, and flexible, facilitating the synthesis of new correspondence algorithms. This paper presents a review of existing surface correspondence algorithms, and shows how they fit into the correspondence framework. It also shows how the framework can be used to analyse and compare existing algorithms and develop new algorithms using the framework's modular structure. Six algorithms, four existing and two new, are implemented using the framework. Each implemented algorithm is used to match a number of surface pairs. Results demonstrate that the correspondence framework implementations are faithful implementations of existing algorithms, and that powerful new surface correspondence algorithms can be created. (C) 2004 Elsevier Inc. All rights reserved.

Improved algorithms for rare event simulation with heavy tails

The estimation of P(S-n > u) by simulation, where S, is the sum of independent. identically distributed random varibles Y-1,..., Y-n, is of importance in many applications. We propose two simulation estimators based upon the identity P(S-n > u) = nP(S, > u, M-n = Y-n), where M-n = max(Y-1,..., Y-n). One estimator uses importance sampling (for Y-n only), and the other uses conditional Monte Carlo conditioning upon Y1,..., Yn-1. Properties of the relative error of the estimators are derived and a numerical study given in terms of the M/G/1 queue in which n is replaced by an independent geometric random variable N. The conclusion is that the new estimators compare extremely favorably with previous ones. In particular, the conditional Monte Carlo estimator is the first heavy-tailed example of an estimator with bounded relative error. Further improvements are obtained in the random-N case, by incorporating control variates and stratification techniques into the new estimation procedures.