Evolving robot sub-behaviour modules using Gene Expression Programming

Many approaches to AI in robotics use a multi-layered approach to determine levels of behaviour from basic operations to goal-directed behaviour, the most well-known of which is the subsumption architecture. In this paper, the performances of the unigenic gene expression programming (ugGEP) and multigenic GEP (mgGEP) in evolving robot controllers for a wall following robot is analysed. Additionally, the paper introduces Regulatory Multigenic Gene Expression Programming (RMGEP), a new evolutionary technique that can be utilised to automatically evolve modularity in robot behaviour. The proposed technique extends the mgGEP algorithm, by incorporating a regulatory gene as part of the GEP chromosome. The regulatory gene, just as in systems biology, determines which of the genes in the chromosome to express and therefore how the controller solves the problem. In the initial experiments, the proposed algorithm is implemented for a robot wall following problem and the results compared to that of ugGEP and mgGEP. In addition to the wall following behaviour, a robot foraging behaviour is implemented with the aim of investigating whether the position of a speci c module (sub-expression tree (ET)) in the overall ET is of importance when coding for a problem.http://link.springer.com/journal/107102016-05-30hb201

A Hidden Markov Model Approach to the Problem of Heuristic Selection in Hyper-Heuristics with a Case Study in High School Timetabling Problems

Operations research is a well-established field that uses computational systems to support decisions in business and public life. Good solutions to operations research problems can make a large difference to the efficient running of businesses and organisations and so the field often searches for new methods to improve these solutions. The high school timetabling problem is an example of an operations research problem and is a challenging task which requires assigning events and resources to time slots subject to a set of constraints. In this article, a new sequence-based selection hyper-heuristic is presented that produces excellent results on a suite of high school timetabling problems. In this study, we present an easy-to-implement, easy-to-maintain, and effective sequence-based selection hyper-heuristic to solve high school timetabling problems using a benchmark of unified real-world instances collected from different countries. We show that with sequence-based methods, it is possible to discover new best known solutions for a number of the problems in the timetabling domain. Through this investigation, the usefulness of sequence-based selection hyper-heuristics has been demonstrated and the capability of these methods has been shown to exceed the state of the art

Tracking digital impact (TDI) tool.

The Tracking Digital Impact (TDI) tool is designed to help researchers, research groups, projects and institutions assess their current and future digital engagement strategies in an objective and informed way to support the development of new and improved strategies that more effectively enable good engagement with businesses, communities, the public, governing bodies and other researchers to facilitate better engagement and greater impact. The TDI tool was developed as part of a JISC funded project which focused on identifying, synthesising and embedding business, community and public (BCE) engagement best practices. The TDI tool examined the best practices at the dot.rural Digital Economies hub at the University of Aberdeen and translated those (accompanied by new guidance) into the TDI tool. Parts of this document were sourced from 'Brief Notes on Social Media for Research' by Jennifer Holden (University of Aberdeen, October, 2012). This document describes the TDI tool and its use

Conceptual framework of a digital twin to evaluate the degradation status of complex engineering systems

Degradation of engineering structures and systems often comes in the form of wear, corrosion, and fracture. These factors progressively bring about performance decay, until the system fails to function satisfactorily. Complex engineering systems (CES) need regular maintenance throughout their operation, along with continuous checks on the health status of components and equipment, within regulatory frameworks. A digital twin paradigm is able to continuously monitor CES, to use this data to update a virtual model of the CES and thus make real-time predictions about future functionality. The purpose of this paper is to introduce a conceptual framework of a digital twin to be applied within the degradation assessment process of a CES. The digital twin framework will aim to gather digital data through a network to plan through-life requirements of the system. Data-driven approaches can be used to predict how degradation evolves over time. The proposed framework will help the decision-making process to better handle maintenance operations and achieve targets such as asset availability and minimised cost

Tracking digital impact (TDI) tool: key questions reference.

This is a quick reference summary of the 'Key Questions' developed as part of the large Tracking Digital Impact (TDI) Tool. Users with experience of digital technologies or have previously completed the TDI tool may find this a useful reference when re-assessing or completing new assessments

Evolutionary computation for wind farm layout optimization

This paper presents the results of the second edition of the Wind Farm Layout Optimization Competition, which was held at the 22nd Genetic and Evolutionary Computation COnference (GECCO) in 2015. During this competition, competitors were tasked with optimizing the layouts of five generated wind farms based on a simplified cost of energy evaluation function of the wind farm layouts. Online and offline APIs were implemented in C++, Java, Matlab and Python for this competition to offer a common framework for the competitors. The top four approaches out of eight participating teams are presented in this paper and their results are compared. All of the competitors' algorithms use evolutionary computation, the research field of the conference at which the competition was held. Competitors were able to downscale the optimization problem size (number of parameters) by casting the wind farm layout problem as a geometric optimization problem. This strongly reduces the number of evaluations (limited in the scope of this competition) with extremely promising results

Summary of evolutionary computation for wind farm layout optimization

This paper presents the results of the second edition of the Wind Farm Layout Optimization Competition, which was held at the 22nd Genetic and Evolutionary Computation COnference (GECCO) in 2015. During this competition, competitors were tasked with optimizing the layouts of ve generated wind farms based on a sim-plied cost of energy evaluation function of the wind farm layouts. Online and oine APIs were implemented in C++, Java, Matlab and Python for this competition to oer a common framework for the competitors. e top four approaches out of eight participating teams are presented in this paper and their results are compared. All of the competitors' algorithms use evolutionary computation

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

Abstract. 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 significant