H-ACO: A Heterogeneous Ant Colony Optimisation approach with Application to the Travelling Salesman Problem

This is the author accepted manuscript. The final version is available from the publisher via the link in this record.Ant Colony Optimization (ACO) is a field of study that mimics the behaviour of ants to solve computationally hard problems. The majority of research in ACO focuses on homogeneous artificial ants although animal behaviour research suggests that heterogeneity of behaviour improves the overall efficiency of ant colonies. Therefore, this paper introduces and analyses the effects of heterogeneity of behavioural traits in ACO to solve hard optimisation problems. The developed approach implements different behaviour by introducing unique biases towards the pheromone trail and local heuristic (the next hop distance) for each ant. The well-known Ant System (AS) and Max-Min Ant System (MMAS) are used as the base algorithms to implement heterogeneity and experiments show that this method improves the performance when tested using several Travelling Salesman Problem (TSP) instances particularly for larger instances. The diversity preservation introduced by this algorithm helps balance exploration-exploitation, increases robustness with respect to parameter settings and reduces the number of algorithm parameters that need to be set.We would like to thank the Faculty of Electronics and Computer Engineering (FKEKK), Technical University of Malaysia Malacca (UTeM) and the Ministry of Higher Education (MoHE) Malaysia for the financial support under the SLAB/SlAI program

Semantic segmentation on small datasets of satellite images using convolutional neural networks

This is the final version. Available from SPIE via the DOI in this recordSemantic segmentation is one of the most popular and challenging applications of deep learning. It refers to the process of dividing a digital image into semantically homogeneous areas with similar properties. We employ the use of deep learning techniques to perform semantic segmentation on high-resolution satellite images representing urban scenes to identify roads, vegetation, and buildings. A SegNet-based neural network with an encoder–decoder architecture is employed. Despite the small size of the dataset, the results are promising. We show that the network is able to accurately distinguish between these groups for different test images, when using a network with four convolutional layers

Ant Colony Optimisation for Exploring Logical Gene-Gene Associations in Genome Wide Association Studies.

In this paper a search for the logical variants of gene-gene interactions in genome-wide association study (GWAS) data using ant colony optimisation is proposed. The method based on stochastic algorithms is tested on a large established database from the Wellcome Trust Case Control Consortium and is shown to discover logical operations between combinations of single nucleotide polymorphisms that can discriminate Type II diabetes. A variety of logical combinations are explored and the best discovered associations are found within reasonable computational time and are shown to be statistically significantThis study makes use of data generated by the Wellcome Trust Case Control Consortium. A full list of the investigators who contributed to the generation of the data is available from http://www.wtccc.org.uk. Funding for the project was provided by the Wellcome Trust under award 076113. The work contained in this paper was funded by an EPSRC First Grant (EP/J007439/1) and we acknowledge their kind support

Offline Learning for Selection Hyper-heuristics with Elman Networks

This is the author accepted manuscript. The final version is available from the publisher via the link in this record.Offline selection hyper-heuristics are machine learning methods that are trained on heuristic selections to create an algorithm that is tuned for a particular problem domain. In this work, a simple selection hyper-heuristic is executed on a number of computationally hard benchmark optimisation problems, and the resulting sequences of low level heuristic selections and objective function values are used to construct an offline learning database. An Elman network is trained on sequences of heuristic selections chosen from the offline database and the network’s ability to learn and generalise from these sequences is evaluated. The networks are trained using a leave-one-out cross validation methodology and the sequences of heuristic selections they produce are tested on benchmark problems drawn from the HyFlex set. The results demonstrate that the Elman network is capable of intra-domain learning and generalisation with 99% confidence and produces better results than the training sequences in many cases. When the network was trained using an interdomain training set, the Elman network did not exhibit generalisation indicating that inter-domain generalisation is a harder problem and that strategies learned on one domain cannot necessarily be transferred to another

Multi-objective Optimisation of a Water Distribution Network with a Sequence-based Selection Hyper-heuristic

This is the author accepted manuscript. The final version is available from the publisher.Multi-objective hyper-heuristics are fast becoming an efficient way of optimising complex problems. The water distribution network design problem is an example of such a problem, and this work employs a recent hyper-heuristic that generates sequences of low-level heuristics to solve the multi-objective water distribution design problem. The results presented are comparable to those generated by state-of-the-art metaheuristics, as well as a single-objective version of the algorithm from the literature. The information revealed from analysing the sequences generated to solve the problem reveal important information about the nature of the problem space that is not available from the metaheuristics, and the entire Pareto front can be explored in a single run as opposed to the multiple runs needed with the original single-objective algorithm

Subset-Based Ant Colony Optimisation for the Discovery of Gene-Gene Interactions in Genome Wide Association Studies

In this paper an ant colony optimisation approach for the discovery of gene-gene interactions in genome-wide association study (GWAS) data is proposed. The subset-based approach includes a novel encoding mechanism and tournament selection to analyse full scale GWAS data consisting of hundreds of thousands of variables to discover associations between combinations of small DNA changes and Type II diabetes. The method is tested on a large established database from the Wellcome Trust Case Control Consortium and is shown to discover combinations that are statistically significant and biologically relevant within reasonable computational time.The work contained in this paper was supported by an EPSRC First Grant (EP/J007439/1). This study makes use of data generated by the Wellcome Trust Case Control Consortium. A full list of the inves- tigators who contributed to the generation of the data is available from http://www.wtccc.org.uk. Funding for the project was provided by the Wellcome Trust under award 076113

A scalable genome representation for neural-symbolic networks

Neural networks that are capable of representing symbolic information such as logic programs are said to be neural-symbolic. Because the human mind is composed of interconnected neurons and is capable of storing and processing symbolic information, neural-symbolic networks contribute towards a model of human cognition. Given that natural evolution and development are capable of producing biological networks that are able to process logic, it may be possible to produce their artificial counterparts through evolutionary algorithms that have developmental properties. The first step towards this goal is to design a genome representation of a neural-symbolic network. This paper presents a genome that directs the growth of neural-symbolic networks constructed according to a model known as SHRUTI. The genome is successful in producing SHRUTI networks that learn to represent relations between logical predicates based on observations of sequences of predicate instances. A practical advantage of the genome is that its length is independent of the size of the network it encodes, because rather than explicitly encoding a network topology, it encodes a set of developmental rules. This approach to encoding structure in a genome also has biological grounding

Developing Decision Tree Models to Create a Predictive Blockage Likelihood Model for Real-World Wastewater Networks

To reduce the blockages occurring on wastewater networks, reducing costs, customer and environmental impact, greater levels of proactive maintenance are being conducted by water and sewerage companies. For effective prioritisation of this maintenance, an accurate model of blockage likelihood is required. This paper presents the development of a model, for provision of a blockage likelihood level and verification using unseen data, based on previous decision tree models constructed using the asset and historical incident data from the wastewater network of Dŵr Cymru Welsh Water. The model has been developed here using the geographical grouping of sewers and the application of ensemble techniques, with the results illustrating the potential benefits which can be derived from these techniques.The work has been conducted as part of a Knowledge Transfer Partnership (KTP) with funding provided by Innovate UK and Dŵr Cymru Welsh Water (DCWW), working in collaboration with the University of Exeter’s Centre for Water Systems (CWS)

Generalising human heuristics in augmented evolutionary water distribution network design optimisation

This is the final versionThe use of evolutionary algorithms (EAs) for finding near optimal water distribution network (WDN) designs is well-established in the literature. Even though these methods have the ability to generate mathematically promising solutions based on defined objective function(s), the resulting solutions are not necessarily suitable for real-world application. This is because of the size, complex and non-linear nature of WDNs, which make it difficult to define important factors that a water engineer or an expert needs to consider during the design process in an objective function. Incorporating an expert in the optimization process has been used to deal with this problem and to guide an EA’s search toward obtaining more practical solutions. Accordingly, this study proposes a methodology for capturing and generalizing engineering expertise in optimizing small/medium WDNs through machine learning techniques, and integrating the resultant heuristic into an EA through its mutation operator to find the optimum design for larger WDNs. The combined interaction from different users on four small /medium benchmark WDNs from the literature were collected and used to train a decision tree model. Seven input features including current pipe diameter, velocity, upstream and downstream head deficient, pipe influence, flow and length are used to train the decision tree for predicting new diameter for a selected pipe. The resultant decision tree model is then applied to a larger network namely Modena to assess the ability of the HDH method. The results demonstrate better performance in comparison with a standard EA approach for finding minimum network cost

Phased Genetic Programming for Application to the Traveling Salesman Problem

This is the author accepted manuscript. The final version is available from ACM via the DOI in this recordThe Traveling Salesman Problem (TSP) is a difficult permutation-based optimisation problem typically solved using heuristics or meta-heuristics which search the solution problem space. An alternative is to find sets of manipulations to a solution which lead to optimality. Hyper-heuristics search this space applying heuristics sequentially, similar to a program. Genetic Programming (GP) evolves programs typically for classification or regression problems. This paper hypothesizes that GP can be used to evolve heuristic programs to directly solve the TSP. However, evolving a full program to solve the TSP is likely difficult due to required length and complexity. Consequently, a phased GP method is proposed whereby after a phase of generations the best program is saved and executed. The subsequent generation phase restarts operating on this saved program output. A full program is evolved piecemeal. Experiments demonstrate that whilst pure GP cannot solve TSP instances when using simple operators, Phased-GP can obtain solutions within 4% of optimal for TSPs of several hundred cities. Moreover, Phased-GP operates up to nine times faster than pure GP.Innovate UKCity Scienc