Our technical report series is intended for early publication of research, and for the publication of extended versions of research papers.

1. A new way of calculating exact exclusive hypervolumes
2. A temporal logic of robustness
3. Statistical exploratory analysis of genetic algorithms: The detrimentality of crossover
4. A fast incremental hypervolume algorithm
5. WinMS: Wireless sensor network-management system. An adaptive policy-based management for wireless sensor networks

A new way of calculating exact exclusive hypervolumes

Lucas Bradstreet, Lyndon While, Luigi Barone

We describe a new method for calculating the exclusive hypervolume of a point 'p' relative to a front 'S'. We use 'p' to bound the objective values of the points in 'S', and subtract the hypervolume of the resulting front from the hypervolume of 'p' alone. Early experiments show great promise for this approach, in both in-line and metric calculations.

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A temporal logic of robustness

John C. McCabe-Dansted, Tim French, Mark Reynolds

It can be desirable to specify polices that require a system to achieve some outcome even if a certain number of failures occur. This paper proposes a logic, RoCTL*, which extends CTL* with operators from Deontic logic, and a novel operator referred to as "Robustly". This novel operator acts as variety of path quantifier allowing us to consider paths which deviate from the desired behaviour of the system. Unlike most path quantifiers, the Robustly operator must be evaluated over a path rather than just a state. The Robustly operator represents the phrase "even if an additional failure occurs now or in the future", and thus the paths quantified over depend upon the failures already on the path. This paper examines the expressivity of this new logic, motivates its use and shows that it is decidable.

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Statistical exploratory analysis of genetic algorithms: The detrimentality of crossover

Andrew Czarn

The traditional concept of a genetic algorithm (GA) is that of selection, crossover and mutation. However, a limited amount of data from the literature has suggested that the niche for the beneficial effect of crossover upon GA performance may be smaller than has traditionally been held. Based upon previous results on not-linear-separable problems we decided to explore this by comparing two test problem suites, one comprising non-rotated functions and the other comprising the same functions rotated by 45 degrees rendering them not-linear-separable.

We find that for the difficult rotated functions the crossover operator was detrimental to the performance of the GA. We conjecture that what makes a problem difficult for the GA is complex and involves factors such as the degree of optimization at local minima due to crossover, the bias associated with the mutation operator and the Hamming Distances present in the individual problems due to the encoding.

Finally, we tested our GA on a real world landscape minimization problem to see if the results obtained would match those from the difficult rotated functions. We find that they match and that the features which make certain of the test functions difficult are also present in the real world problem. Statistical Exploratory Analysis of Genetic Algorithms: The Detrimentality of Crossover.

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A fast incremental hypervolume algorithm

Lucas Bradstreet, Lyndon While, Luigi Barone

When hypervolume is used as part of the selection or archiving process in a multi-objective evolutionary algorithm (MOEA), it is necessary to determine which solutions contribute the least hypervolume to a front. Little focus has been placed on algorithms that quickly determine these solutions and there are no fast algorithms designed specifically for this purpose. We describe an algorithm, IHSO, that quickly determines a solution's contribution. Furthermore, we describe heuristics that re-order objectives to minimise the work required for IHSO to calculate a solution's contribution. Lastly, we describe and analyse search techniques that reduce the hypervolume calculations required for solutions other than the least contributing solution. Combined, these techniques allow MOEAs to calculate hypervolume in-line in increasingly complex and large fronts in many objectives.

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WinMS: Wireless sensor network-management system. An adaptive policy-based management for wireless sensor networks

Winnie Louis Lee, Amitava Datta, Rachel Cardell-Oliver

Wireless sensor networks are highly dynamic, prone to faults, and typically deployed in remote and harsh conditions.  We propose WinMS, an adaptive policy-based sensor network management system that provides self-management for maintaining the performance of the network and achieving effective networked node operations without human intervention.  WinMS adapts to changing network conditions by allowing the network to reconfigure itself according to current events as well as predicting future events.  WinMS architecture consists of a schedule-driven MAC protocol that collects and disseminates management data, to and from sensor nodes in a data gathering tree, a local network management scheme that provides autonomy for individual sensor nodes to perform management functions according to their neighborhood network states, and a central network management scheme that uses the central manager with a global knowledge of the network to reliably execute corrective and preventive management maintenance.  WinMS proposes a novel management function, called systematic resource transfer that allows time slots (resources) from one part of the network to be transferred to another part. This paper presents analysis and simulation results of the proposed function, showing that it supports non-uniform and reactive sensing in different parts of a network.

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