ABSTRACT
9.1 Introduction
Evolutionary algorithms employ computational models of natural evolutionary processes in developing problem-solving systems (Goldberg 1989). This form of search evolves throughout generations by enhancing the attributes of potential solutions and simulating the natural population of biological entities. In this chapter, several types of evolutionary algorithms, including genetic algorithms (GA), genetic programming (GP), and particle swarm optimization (PSO), are delineated. Three real applications of evolutionary algorithms are also demonstrated. The first application case study presents the use of GP for modelling and prediction of algal blooms in Tolo Harbour, Hong Kong. The second application is for flood forecasting at a prototype channel reach of the Yangtze River in China by employing a GA-based artificial neural network (ANN) in comparison with several benchmarking models. The third application is the use of a PSO training algorithm for ANNs in stage prediction of Shing Mun River.
