Lecture 9 Evolutionary Computation: Genetic algorithms ■ Introduction, or can evolution be intelligent? ■ Simulation of natural evolution ■ Genetic algorithms ■ Case study: maintenance scheduling with genetic algorithms ■ Summary  Negnevitsky, Pearson Education, 2002 1 Can evolution be intelligent? ■ Intelligence can be defined as the capability of a system to adapt its behaviour to ever-changing environment. According to Alan Turing, the form or appearance of a system is irrelevant to its intelligence. ■ Evolutionary computation simulates evolution on a computer. The result of such a simulation is a series of optimisation algorithms, usually based on a simple set of rules. Optimisation iteratively improves the quality of solutions until an optimal, or at least feasible, solution is found.  Negnevitsky, Pearson Education, 2002 2 ■ The behaviour of an individual organism is an inductive inference about some yet unknown aspects of its environment. If, over successive generations, the organism survives, we can say that this organism is capable of learning to predict changes in its environment. ■ The evolutionary approach is based on computational models of natural selection and genetics. We call them evolutionary computation, an umbrella term that combines genetic algorithms, evolution strategies and genetic programming.  Negnevitsky, Pearson Education, 2002 3 Simulation of natural evolution ■ On 1 July 1858, Charles Darwin presented his theory of evolution before the Linnean Society of London. This day marks the beginning of a revolution in biology. ■ Darwin’s classical theory of evolution, together with Weismann’s theory of natural selection and Mendel’s concept of genetics, now represent the neo-Darwinian paradigm.  Negnevitsky, Pearson Education, 2002 4 ■ Neo-Darwinism is based on processes of reproduction, mutation, competition and selection. The power to reproduce appears to be an essential property of life. The power to mutate is also guarantee

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