• Optimization methods based upon natural or biological processes
• Need to be able to parameterise a solution and determine a cost function (good or bad solution?)
• No need to find gradients No need to find gradients
• Directed random search
• Not guaranteed to find the exact optimum
• Wide range of uses
• Particularly good when cost calculation is small, constraints exist and function is non continuous.

Methods considered
- Genetic Algorithms (binary)
- Genetic Algorithms (real)
- Population Based Incremental Learning
- Particle Swarm Optimisation
- Particle Swarm Optimisation
- Evolutionary Growth
- Ant Colony Optimisation
• Other common methods
- Simulated Annealing
- Bacterial Optimisation
- Evolutionary Programming