Analysing heuristic subsequences for offline hyper-heuristic learning

This is the author accepted manuscript. The final version is available from ACM via the DOI in this recordThe paper explores the impact of sequences of search operationson the performance of an optimiser through the use of log returnsand a database of sequences. The study demonstrates that althoughthe performance of individual perturbation operators is important,understanding their performance in sequence provides greater op-portunity for performance improvements within and across opera-tions research domains

Markov Chain Selection Hyper-heuristic for the Optimisation of Constrained Magic Squares

UKCI 2015: UK Workshop on Computational Intelligence, University of Exeter, UK, 7-9 September 2015A square matrix of size n × n, containing each of the numbers (1, . . . , n2) in which every row, column and both diagonals has the same total is referred to as a magic square. The problem can be formulated as an optimisation problem where the task is to minimise the deviation from the magic square constraints and is tackled here by using hyper-heuristics. Hyper-heuristics have recently attracted the attention of the artificial intelligence, operations research, engineering and computer science communities where the aim is to design and develop high level strategies as general solvers which are applicable to a range of different problem domains. There are two main types of hyper-heuristics in the literature: methodologies to select and to generate heuristics and both types of approaches search the space of heuristics rather than solutions. In this study, we describe a Markov chain selection hyper-heuristic as an effective solution methodology for optimising constrained magic squares. The empirical results show that the proposed hyper-heuristic is able to outperform the current state-of-the-art method

An analysis of heuristic subsequences for offline hyper-heuristic learning

This is the final version. Available on open access from Springer Verlag via the DOI in this recordA selection hyper-heuristic is used to minimise the objective functions of a well-known set of benchmark problems. The resulting sequences of low level heuristic selections and objective function values are used to generate a database of heuristic selections. The sequences in the database are broken down into subsequences and the mathematical concept of a logarithmic return is used to discriminate between “effective” subsequences, which tend to decrease the objective value, and “disruptive” subsequences, which tend to increase the objective value. These subsequences are then employed in a sequenced based hyper-heuristic and evaluated on an unseen set of benchmark problems. Empirical results demonstrate that the “effective” subsequences perform significantly better than the “disruptive” subsequences across a number of problem domains with 99% confidence. The identification of subsequences of heuristic selections that can be shown to be effective across a number of problems or problem domains could have important implications for the design of future sequence based hyper-heuristics

Multi-objective pipe smoothing genetic algorithm for water distribution network design

Session S6-03, Special Session: Evolutionary Computing in Water Resources Planning and Management IIIThis paper describes the formulation of a Multi-objective Pipe Smoothing Genetic Algorithm (MOPSGA) and its application to the least cost water distribution network design problem. Evolutionary Algorithms have been widely utilised for the optimisation of both theoretical and real-world non-linear optimisation problems, including water system design and maintenance problems. In this work we present a pipe smoothing based approach to the creation and mutation of chromosomes which utilises engineering expertise with the view to increasing the performance of the algorithm whilst promoting engineering feasibility within the population of solutions. MOPSGA is based upon the standard Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and incorporates a modified population initialiser and mutation operator which directly targets elements of a network with the aim to increase network smoothness (in terms of progression from one diameter to the next) using network element awareness and an elementary heuristic. The pipe smoothing heuristic used in this algorithm is based upon a fundamental principle employed by water system engineers when designing water distribution pipe networks where the diameter of any pipe is never greater than the sum of the diameters of the pipes directly upstream resulting in the transition from large to small diameters from source to the extremities of the network. MOPSGA is assessed on a number of water distribution network benchmarks from the literature including some real-world based, large scale systems. The performance of MOPSGA is directly compared to that of NSGA-II with regard to solution quality, engineering feasibility (network smoothness) and computational efficiency. MOPSGA is shown to promote both engineering and hydraulic feasibility whilst attaining good infrastructure costs compared to NSGA-II

Artificial development of connections in SHRUTI networks using a multi-objective genetic algorithm

SHRUTI is a model of how first-order logic can be represented and reasoned upon using a network of spiking neurons in an attempt to model the brain’s ability to perform reasoning. This paper extends the biological plausibility of the SHRUTI model by presenting a genotype representation of connections in a SHRUTI network using indirect encoding and showing that networks represented in this way can be generated by an evolutionary process

Interactive 3D visualisation of optimisation for water distribution systems

Session S6-03, Special Session: Evolutionary Computing in Water Resources Planning and Management IIIThis project investigates the use of modern 3D visualisation techniques to enable the interactive analysis of water distribution systems with the aim of providing the engineer with a clear picture of the problem and thus aid the overall design process. Water distribution systems are complex entities that are difficult to model and optimise as they consist of many interacting components each with a set of considerations to address, hence it is important for the engineer to understand and assess the behaviour of the system to enable its effective design and optimisation. This paper presents a new three-dimensional representation of pipe based water systems and demonstrates a range of innovative methods to convey information to the user. The system presented not only allows the engineer to visualise the various parameters of a network but also allows the user to observe the behaviour and progress of an iterative optimisation method. This paper contains examples of the combination of the interactive visualisation system and an evolutionary algorithm enabling the user to track and visualise the actions of the algorithm down to an individual pipe diameter change. It is proposed that this interactive visualisation system will provide engineers an unprecedented view of the way in which optimisation algorithms interact with a network model and may pave the way for greater interaction between engineer, network and optimiser in the futur

Evolution of connections in SHRUTI networks

SHRUTI is a model of how predicate relations can be represented and reasoned upon using a network of spiking neurons, attempting to model the brain’s ability to perform reasoning using as biologically plausible a means as possible. This paper extends the biological plausibility of the SHRUTI model by presenting a genotype representation of connections in a SHRUTI network using indirect encoding and showing that working networks represented in this way can be produced through an evolutionary process. A multi-objective algorithm is used to minimise the error and the number of weight changes that take place as a network learn

Genetic programming for cellular automata urban inundation modelling

Session S5-02, Special Session: Computational Intelligence in Data Driven and Hybrid Models and Data Analysis IIRecent advances in Cellular Automata (CA) represent a new, computationally efficient method of simulating flooding in urban areas. A number of recent publications in this field have shown that CAs can be much more computationally efficient than methods that use standard shallow water equations (Saint Venant/Navier-Stokes equations). CAs operate using local statetransition rules that determine the progression of the flow from one cell in the grid to another cell, and in a number of publications the Manning’s Formula is used as a simplified local state transition rule. Through the distributed interactions of the CA, computationally simplified urban flooding can be simulated, although these methods are limited by the approximation represented by the Manning’s formula. An alternative approach is to learn the state transition rule using an artificial intelligence approach. One such approach is Genetic Programming (GP) that has the potential to be used to optimise state transition rules to maximise accuracy and minimise computation time. In this paper we present some preliminary findings on the use of genetic programming (GP) for deriving these rules automatically. The experimentation compares GP-derived rules with human created solutions based on the Manning’s formula and findings indicate that the GP rules can improve on these approach

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

Human-Evolutionary Problem Solving through Gamification of a Bin-Packing Problem

This is the author accepted manuscript. The final version is available from ACM via the DOI in this recordMany complex real-world problems such as bin-packing are optimised using evolutionary computation (EC) techniques. Involving a human user during this process can avoid producing theoretically sound solutions that do not translate to the real world but slows down the process and introduces the problem of user fatigue. Gamification can alleviate user boredom, concentrate user attention, or make a complex problem easier to understand. This paper explores the use of gamification as a mechanism to extract problem-solving behaviour from human subjects through interaction with a gamified version of the bin-packing problem, with heuristics extracted by machine learning. The heuristics are then embedded into an evolutionary algorithm through the mutation operator to create a human-guided algorithm. Experimentation demonstrates that good human performers augment EA performance, but that poorer performers can be detrimental to it in certain circumstances. Overall, the introduction of human expertise is seen to benefit the algorithm