Post enrolment based course timetabling: a description of the problem model used for track two of the second International Timetabling Competition

In this paper we give a detailed description of the problem model used in track-two of the second International Timetabling Competition, 2007-2008 www.cs.qub.ac.uk/itc2007/). This model is an extension of that used in the first timetabling competition, and we discuss the rationales behind these extensions. We also describe in detail the criteria that are used for judging solution quality and discuss other issues that are related to this. Finally we go over some of the strengths and limitations of the model. This paper can be regarded as the official documentation for track-two of the timetabling competition

A Lifelong Learning Hyper-heuristic Method for Bin Packing.

We describe a novel Hyper-heuristic system which continuously learns over time to solve a combinatorial optimisation problem. The system continuously generates new heuristics and samples problems from its environment; representative problems and heuristics are incorporated into a self-sustaining network of interacting entities in- spired by methods in Artificial Immune Systems.The network is plastic in both its structure and content leading to the following properties: it exploits existing knowl- edge captured in the network to rapidly produce solutions; it can adapt to new prob- lems with widely differing characteristics; it is capable of generalising over the prob- lem space. The system is tested on a large corpus of 3968 new instances of 1D-bin packing problems as well as on 1370 existing problems from the literature; it shows excellent performance in terms of the quality of solutions obtained across the datasets and in adapting to dynamically changing sets of problem instances compared to pre- vious approaches. As the network self-adapts to sustain a minimal repertoire of both problems and heuristics that form a representative map of the problem space, the system is further shown to be computationally efficient and therefore scalable

Finding feasible timetables using group-based operators.

This paper describes the applicability of the so-called "grouping genetic algorithm" to a well-known version of the university course timetabling problem. We note that there are, in fact, various scaling up issues surrounding this sort of algorithm and, in particular, see that it behaves in quite different ways with different sized problem instances. As a by-product of these investigations, we introduce a method for measuring population diversities and distances between individuals with the grouping representation. We also look at how such an algorithm might be improved: first, through the introduction of a number of different fitness functions and, second, through the use of an additional stochastic local-search operator (making in effect a grouping memetic algorithm). In many cases, we notice that the best results are actually returned when the grouping genetic operators are removed altogether, thus highlighting many of the issues that are raised in the stud

Representations and evolutionary operators for the scheduling of pump operations in water distribution networks.

Reducing the energy consumption of water distribution networks has never had more significance. The greatest energy savings can be obtained by carefully scheduling the operations of pumps. Schedules can be defined either implicitly, in terms of other elements of the network such as tank levels, or explicitly by specifying the time during which each pump is on/off. The traditional representation of explicit schedules is a string of binary values with each bit representing pump on/off status during a particular time interval. In this paper, we formally define and analyze two new explicit representations based on time-controlled triggers, where the maximum number of pump switches is established beforehand and the schedule may contain less switches than the maximum. In these representations, a pump schedule is divided into a series of integers with each integer representing the number of hours for which a pump is active/inactive. This reduces the number of potential schedules compared to the binary representation, and allows the algorithm to operate on the feasible region of the search space. We propose evolutionary operators for these two new representations. The new representations and their corresponding operations are compared with the two most-used representations in pump scheduling, namely, binary representation and level-controlled triggers. A detailed statistical analysis of the results indicates which parameters have the greatest effect on the performance of evolutionary algorithms. The empirical results show that an evolutionary algorithm using the proposed representations improves over the results obtained by a recent state-of-the-art Hybrid Genetic Algorithm for pump scheduling using level-controlled triggers

Post enrolment based course timetabling: a description of the problem model used for track two of the second International Timetabling Competition

In this paper we give a detailed description of the problem model used in track-two of the second International Timetabling Competition, 2007-2008 www.cs.qub.ac.uk/itc2007/). This model is an extension of that used in the first timetabling competition, and we discuss the rationales behind these extensions. We also describe in detail the criteria that are used for judging solution quality and discuss other issues that are related to this. Finally we go over some of the strengths and limitations of the model. This paper can be regarded as the official documentation for track-two of the timetabling competition

On the comparison of initialisation strategies in differential evolution for large scale optimisation

Differential Evolution (DE) has shown to be a promising global opimisation solver for continuous problems, even for those with a large dimensionality. Different previous works have studied the effects that a population initialisation strategy has on the performance of DE when solving large scale continuous problems, and several contradictions have appeared with respect to the benefits that a particular initialisation scheme might provide. Some works have claimed that by applying a particular approach to a given problem, the performance of DE is going to be better than using others. In other cases however , researchers have stated that the overall performance of DE is not going to be affected by the use of a particular initialisation method. In this work, we study a wide range of well-known initialisation techniques for DE. Taking into account the best and worst results, statistically significant differences among considered initialisation strategies appeared. Thus, with the aim of increasing the probability of appearance of high-quality results and/or reducing the probability of appearance of low-quality ones, a suitable initialisation strategy, which depends on the large scale problem being solved, should be selected

This pervasive day: creative Interactive methods for encouraging public engagement with FET research

This paper describes a case study of a programme of interactive public engagement activities presented by the PerAda Co-ordination Action project (FET Proactive Initiative on Pervasive Adaptation) [1] in 2011. The intention behind these events was to inform an interested public audience about the technology and design of pervasive and adaptive computing systems in general, and PerAda research in particular. Additional explicit aims were to widen debate about the socio-technical implications of this research, and to assess public attitudes to its potential applications in an accessible, engaging and creative manner

Heaven and Hell: visions for pervasive adaptation

With everyday objects becoming increasingly smart and the “info-sphere” being enriched with nano-sensors and networked to computationally-enabled devices and services, the way we interact with our environment has changed significantly, and will continue to change rapidly in the next few years. Being user-centric, novel systems will tune their behaviour to individuals, taking into account users’ personal characteristics and preferences. But having a pervasive adaptive environment that understands and supports us “behaving naturally” with all its tempting charm and usability, may also bring latent risks, as we seamlessly give up our privacy (and also personal control) to a pervasive world of business-oriented goals of which we simply may be unaware

Emerging Artificial Societies Through Learning

The NewTies project is implementing a simulation in which societies of agents are expected to de-velop autonomously as a result of individual, population and social learning. These societies are expected to be able to solve environmental challenges by acting collectively. The challenges are in-tended to be analogous to those faced by early, simple, small-scale human societies. This report on work in progress outlines the major features of the system as it is currently conceived within the project, including the design of the agents, the environment, the mechanism for the evolution of language and the peer-to-peer infrastructure on which the simulation runs.Artificial Societies, Evolution of Language, Decision Trees, Peer-To-Peer Networks, Social Learning