This chapter introduces the concept of optimisation, which involves selecting the best solution from a set of alternatives based on specific criteria. It explains that optimisation problems can be very complex, making it impossible to find the absolute best solution within a reasonable timeframe, especially for problems with large solution spaces. These problems are classified into discrete and continuous optimisation, with combinatorial optimisation being a subset of discrete optimisation. The chapter highlights the Travelling Salesman Problem (TSP) as a classic example of a combinatorial optimisation problem, where the goal is to find the shortest route between multiple cities, and the Protein Folding problem, a well-known continuous optimisation problem, where the goal is to find the protein structure of minimal energy. The chapter also discusses heuristics and metaheuristics as approximate methods for solving optimisation problems. These approaches involve intelligently sampling the solution space to find good enough solutions, given time and computational resource limitations. The No Free Lunch (NFL) Theorem states that there is no universally best optimisation strategy, as the performance of heuristics varies depending on the problem. The chapter concludes by emphasising the challenges of optimisation due to limited problem-domain knowledge, the complexity of search spaces, and time-varying or imprecise fitness landscapes.