A Brief Introduction to Evolutionary and other Nature‐Inspired Algorithms

This chapter presents an introduction of evolutionary and other nature-inspired computation. The most prominent types of evolutionary computation (EC) are genetic algorithms (GA), genetic programming (GP), evolutionary strategy (ES) and evolutionary programming (EP). Many computational algorithms and problem-solving techniques, commonly known as swarm intelligence, have been developed by simulating the coordination and teamwork strategies in social insects. Elements comprising GAs are data representation, selection, crossover, mutation, and alternation of generations. GP can be used to apply evolutionary approaches to automatic code generation and problem solving by artificial intelligence. ES in its early days differed from GAs in the following two ways: mutation is used as the main operator and real number expressions are handled. The chapter also discusses relative advantages/disadvantages and application areas of these algorithms. Numerous applications of EC exist in structural engineering, architectural design, environmental engineering, geotechnical and water resource engineering