Details of Grant 

EPSRC Reference:	GR/J70857/01
Title:	MULTIOBECTIVE GENETIC ALGORITHMS
Principal Investigator:	Fleming, Professor PJ
Other Investigators:
Researcher Co-Investigators:
Project Partners:
Department:	Automatic Control and Systems Eng
Organisation:	University of Sheffield
Scheme:	Standard Research (Pre-FEC)
Starts:	01 September 1994	Ends:	31 March 1998	Value (£):	119,564
EPSRC Research Topic Classifications:	Design & Testing Technology
EPSRC Industrial Sector Classifications:	Aerospace, Defence and Marine Information Technologies
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Panel History:
Summary on Grant Application Form
i) To develop an effective approach for solving multi-objective optimisation problems using genetic algorithms.ii) To devise appropriate parallel algorithms and architectures for multi-objective genetic algorithms (MOGAs).iii) To test these schemes on practical engineering design problems.Progress:Multi-objective optimisation involves the simultaneous optimisation of a set of, often competing, objectives. In the design of an engineering system, for example, such objectives might comprise a mixture of performance,quality and economic measures. The solution to such a problem consists of a set of non inferior solutions in which no one solution is dominant over another. This non-inferior solution set represents a tradeoff surface from which the decision maker (DM) can gain an understanding of the options available and select candidate solutions.One promising genetic algorithm application area that has been little researched until recently is that of multi-objective optimisation. Operating on populations of solution estimates, a GA approach can evolve a set of solutions which confers an immediate benefit over alternative conventional MO methods which can produce only one point at a time.Four months work on the project has now been completed. A literature review has been undertaken and a survey paper has been submitted to Evolutionary Computation. The GA Toolbox (used within MATLAB) has been modified to support experimentation with MOGA strategies. This is enabling us to efficiently determine appropriate GA parameters (e.g. population size and mutation rates) and strategies (e.g. selection, crossover). Coding is also an important area which is being studied at present. MOGA strategies are being devised and exercised on test problems. Good contact has already been established with industry (notably Rolls-Royce Applied Science Laboratories) to identify useful case studies.
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Organisation Website:	http://www.shef.ac.uk