A fuzzy genetic algorithm for driver scheduling
This paper presents a hybrid genetic algorithm (GA) for the bi-objective public transport driver scheduling problem. A greedy heuristic is used, which constructs a schedule by sequentially selecting shifts, from a very large set of pre-generated legal potential shifts, to cover the remaining work. Individual shifts and the schedule as a whole have to be evaluated in the process. Fuzzy set theory is applied on such evaluations. For individual shifts, their structural efficiency is assessed by fuzzified criteria identified from practical knowledge of the problem domain. A GA is used to derive a near-optimal weight distribution amongst the fuzzified criteria, so that a single-valued weighted evaluation can be computed for each shift. The corresponding schedule constructed utilising the weight distribution is evaluated by the GA’s fitness function, in which the two objectives of minimising the number of shifts and minimising the total cost are formulated as a fuzzy goal. Comparative results on real-world problems are presented.

title: The Application of Bayesian Optimization and Classifier Systems in Nurse Scheduling
abstract: Two ideas taken from Bayesian optimization and classifier systems are presented for personnel scheduling based on choosing a suitable scheduling rule from a set for each person’s assignment. Unlike our previous work of using genetic algorithms whose learning is implicit, the learning in both approaches is explicit, i.e. we are able to identify building blocks directly. To achieve this target, the Bayesian optimization algorithm builds a Bayesian network of the joint probability distribution of the rules used to construct solutions, while the adapted classifier system assigns each rule a strength value that is constantly updated according to its usefulness in the current situation. Computational results from 52 real data instances of nurse scheduling demonstrate the success of both approaches. It is also suggested that the learning mechanism in the proposed approaches might be suitable for other scheduling problems.

title: A Self-Adjusting Algorithm for Driver Scheduling
abstract: Public transport driver scheduling is a world wide problem, which is NP-hard. Although some mathematically based methods are being used in the transport industry, there is still much scope for improvements. This paper presents a novel evolutionary approach that simulates the self-adjusting process on a single schedule. Five factors characterized by fuzzy membership functions are first aggregated to evaluate the shift structure. This evaluating function is incorporated into a constructing heuristic to make shift selection. A self-adjusting algorithm is then designed to guide the constructing heuristic to improve a given initial schedule iteratively. In each generation an unfit portion of the working schedule is removed. Broken schedules are repaired by the constructing heuristic until stopping condition is met. Experimental results on real-world driver scheduling problems has demonstrated the success of the proposed approach.

title: A Construction for General and Efficient Oblivious Commitment Based Envelope Protocols
abstract: The notion of Oblivious Commitment Based Envelope (OCBE) was recently proposed; it enables attribute-based access control without revealing any information about the attributes. Previous OCBE protocols are designed by taking zero-knowledge proof protocols that prove a committed value satisfies some property and changing the protocols so that instead of one party proving to the other party, the two parties compute two keys that agree if and only if the committed value indeed satisfy the property. In this paper, we introduce a more general approach for designing OCBE protocols that uses zero-knowledge proof protocols in a black-box fashion. We present a construction such that given a zero-knowledge proof protocol that proves a committed value satisfies a predicate, we have an OCBE protocol for that predicate with constant additional cost. Compared with previous OCBE protocols, our construction is more general, more efficient, and has wide applicability.

title: Evolvable Fuzzy System for ATM Cell Scheduling
abstract: In this paper, we propose an evolvable fuzzy system for ATM cell scheduling. When the scenarios of cell flows in an ATM network change dramatically, traditional scheduling algorithms, first-in-first-out (FIFO) and static priority, which employ static switching scheme may see their efficiency deteriorate. An alternative is to use dynamically weighted priority, which changes the priority based on the cell delay. Still, the algorithm per se is fixed and on the hardware level, nothing changes. Our goal in this paper is to achieve hardware adaptability and demonstrate its applicability in ATM switching. To do this, we proposed an evolvable fuzzy system . With evolvable fuzzy system (EFS), the system undergoes transformation in response to changes in the cell patterns in order to maintain operational efficiency. We envisage that such a system is potentially useful for demanding real-time dynamic control applications.

title: Unsupervised Multiresolution Segmentation for Images with Low Depth of Field
abstract: Unsupervised segmentation of images with low depth of field (DOF) is highly useful in various applications. This paper describes a novel multiresolution image segmentation algorithm for low DOF images. The algorithm is designed to separate a sharply focused object-of-interest from other foreground or background objects. The algorithm is fully automatic in that all parameters are image independent. A multi-scale approach based on high frequency wavelet coefficients and their statistics is used to perform context-dependent classification of individual blocks of the image. Unlike other edge-based approaches, our algorithm does not rely on the process of connecting object boundaries. The algorithm has achieved high accuracy when tested on more than 100 low DOF images, many with inhomogeneous foreground or background distractions. Compared with he state of the art algorithms, this new algorithm provides better accuracy at higher speed.

title: Mining risk patterns in medical data
abstract: In this paper, we discuss a problem of finding risk patterns in medical data. We define risk patterns by a statistical metric, relative risk, which has been widely used in epidemiological research. We characterise the problem of mining risk patterns as an optimal rule discovery problem. We study an anti-monotone property for mining optimal risk pattern sets and present an algorithm to make use of the property in risk pattern discovery. The method has been applied to a real world data set to find patterns associated with an allergic event for ACE inhibitors. The algorithm has generated some useful results for medical researchers.

title: Groups of connected objects