Towards the Development of an Integrated Multi-Objective Solution and Analysis System

This paper details some of the issues investigated and experiments conducted by the authors in the course of their design of a integrated multi-objective solution and analysis system. The role to which the concept of multiple objectives can be incorporated into the genetic algorithm framework is examined. The composition of a multi-objective fitness function is discussed, as well as its implications for the genetic operators such as selection, crossover and mutation. Current work on such issues as representation of the efficient set by genetic algorithms are reviewed. A non-aggregating randomised selection algorithm is given and illustrated by means of an example. The use of genetic algorithms for the solution of difficult goal programming models is investigated. A representative non-linear goal programming model is solved under various genetic algorithm parameter options in order to demonstrate the type of parameter sensitivity that can occur when using genetic algorithms as a solution tool.

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