This chapter starts with an overview of the basic mechanics of GAs and highlights their major differences when compared to traditional and enumerative search and optimisation techniques. The main components of the GA are then described in some detail and various alternative approaches to the major procedures are considered. After a brief discussion of other evolutionary algorithms, parallel models of the GA are then considered and it is shown how it may be possible to improve the performance of the algorithm even when it is implemented on a sequential computer. Next, considerations commonly arising in engineering systems and the manner in which they may be treated through the application of GAs are discussed. Finally, an example of the use of a GA in aircraft engine controller configuration design is presented to demonstrate how GAs may be applied to problems for which there are currently no other direct methods of solution.

This chapter discusses how an existing GA can be modified and set up to explore the relevant trade-offs between multiple objectives with a minimum of effort. Although Pareto and Pareto-like ranking schemes can be easily implemented, current guidelines on the associated set up of techniques such as sharing and mating restriction are intricate and/or based on more or less rough assumptions about the cost landscape, which has not contributed to their popularity.

There has been a great deal of interesting work published on evolving neural networks in the last few years, some of which is mentioned below. Nearly all previous work, however, has concentrated on evolving a particular neural network to solve a particular problem. When a suitable solution is evolved, then all we have is a suitable solution for a particular problem. The work reported here offers a significant departure from that theme, and presents a simple system which allows the evolution of scaleable neural architectures. This is important for two reasons: Evolutionary search is computationally expensive. When evolving solutions to complex problems, it might be better to evolve solutions to small examples of the problem, then for the real application, scale up some of the best evolved solutions to the real problem size. Having evolved good solutions to a problem, it would be good to apply them to similar problems of a different size. By allowing this, we allow maximum possible reuse of neural network modules.

Scheduling is the allocation of shared resources over time to competing activities, and has been the subject of a significant amount of literature in the operations research field. Emphasis has been on investigating machine scheduling problems where jobs represent activities and machines represent resources; each machine can process at most one job at a time. This chapter reviews a variety of GA applications to the JSSP. We begin our discussion by formulating the JSSP by a disjunctive graph. We then look at domain independent binary and permutation representations, followed by an active schedule representation with GT crossover and the genetic enumeration method. Section 7.7 discusses a method for integrating local optimisation directly into GAs. Section 7.8 discusses performance comparison using the well known Muth and Thompson benchmark and the more difficult ten tough problems.

Characteristics of aerodynamic optimisation have been discussed through wing shape design problems. It has been demonstrated that distribution of the objective function can be extremely rough even in a simplified problem. In such a situation, GA (genetic algorithm) is expected to be more effective than a simple hill-climbing strategy. Three optimisation algorithms, the GM (gradient-based method), SA (simulated annealing) and GA, were first applied to the airfoil shape design using the approximation concept to compare their performances. Although GA is time consuming, its result is superior to those of the others. Since the other algorithms will require many trials starting from various initial designs to obtain a comparable result, they will not have any advantage in efficiency. The result suggests that GA is the best option for aerodynamic optimisation.