Citation:
Ashutosh Tiwari and Rajkumar Roy. Challenges in real world optimisation using evolutionary computing. Decision Engineering Report Series, Cranfield University, 2004
Abstract:
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