Centre for Decision Engineering

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'Decision Engineering' is an emerging discipline that focuses on developing tools and techniques for informed operational and business decision-making within industry by utilising data and information available at the time (facts) and distributed organisational knowledge.

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Browsing Centre for Decision Engineering by Subject "evolutionary algorithm"

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    Challenges in real world optimisation using evolutionary computing
    (2004-11-10T00:00:00Z) Tiwari, Ashutosh; Roy, Rajkumar
    Challenges in real world optimisation using evolutionary computing With rising global competition, it is becoming increasingly more important for industry to optimise its activities. However, the complexity of real-life optimisation problems has prevented industry from exploiting the potential of optimisation algorithms. Industry has therefore relied on either trial-and-error or over- simplification for dealing with its optimisation problems. This has led to the loss of opportunity for improving performance, saving costs and time. The growth of research in the field of evolutionary computing has been encouraged by a desire to harness this opportunity. There are a number of benefits of evolutionary-based optimisation that justify the effort invested in this area. The most significant advantage lies in the gain of flexibility and adaptability to the task in hand, in combination with robust performance and global search characteristics. This report presents the proceedings of the workshop on ‘Challenges in Real World Optimisation Using Evolutionary Computing’. This workshop is organised in association with the Eighth International Conference on Parallel Problem Solving from Nature (PPSN VIII) held in Birmingham (UK) on 18- 22 September 2004. The aim of this workshop is to explore the use of evolutionary computing techniques for solving real-life optimisation problems. It is the purpose of this workshop to bring together researchers working in the area of industrial application of evolutionary-based computing techniques such as genetic algorithms, evolutionary programming, genetic programming and evolutionary strategies. The workshop provides a great opportunity for presenting and disseminating latest work in optimisation applications of evolutionary computing in varied industry sectors and application areas, e.g. manufacturing, service, bioinformatics and retail. It provides a forum for identifying and exploring the key issues that affect the industrial application of evolutionary-based computing techniques.  This report presents three papers from the workshop. The first paper examines the possibilities of train running time control using genetic algorithms for the minimisation of energy costs in DC rapid transit systems. The second paper provides an overview of soft computing techniques used in the lead identification and optimisation stages of the drug discovery process. The third paper proposes a micro-evolutionary programming technique for optimisation of continuous