TABLE OF CONTENTS Systems engineers are increasingly faced with problems for which traditional tools and techniques are ill suited. Such problems are posed by systems that are complex, uncertain and not conducive to deterministic analysis. Modern tools and techniques are being applied in these subject areas in order to address such problems. This chapter introduces the multi-objective genetic algorithm (MOGA) of Fonseca and Fleming [1] as one of the tools for addressing these problems. Background to genetic algorithms (GAs) is given in terms of their operators and abstraction of the problem domain. Multi-objective optimisation is introduced using the concept of Pareto dominance and trade-off between competing objectives. Visualisation techniques are illustrated in which many-objective problems may be handled and preference articulation may be implemented. The motivation for using GAs for multi-objective problems such as control systems design and systems identification is given. The chapter concludes with case studies illustrating the use of MOGA for control system design and multi- objective genetic programming (MOGP) for system identification.
Genetic algorithms (GAs) are stochastic global search algorithms that form a subset of evolutionary algorithms (EAs). The search method is drawn from the principles of natural selection [2] and population genetics [3]. GAs were first proposed by Holland [4] and popularised by Goldberg [5]. GAs work with a population of potential solutions to a problem (see Figure 3.1). This population-based approach results in a parallel search strategy in which...
