# Optimization

Optimisation is the method of obtaining the most effective and/or efficient solution to a given problem. There are many cases in which we can use genetic algorithms in order to optimize, such as choosing parameters, these could be setting the population size and keeping mutation rate. As well as evaluating the fitness of each individual to reproduce.

An example of optimization would be that if trying to find the best airplane wing, parameters would have to be set such as the wingspan that is allowed and then once these were defined, A fitness value would be defined in this case perhaps referring to the aerodynamics of the wing and then via the process of natural selection would occur by comparing the values to the fitness value in order to decide on the "best" solution. Therefore finding the most aerodynamic airplane wing.

Another example would be in trying to find the best combination of things to put in your bag with a limit of 3 kilograms, the way to optimize this would be by finding the combination which would best achieve the 3 kilogram limit. This can further be optimized through the use of genetic algorithms and through the use of a fitness value which is the 3 kilogram limit will then find the best solution through natural selection as the solutions with the highest fitness values will then "mate" and they will crossover in order to make an even better solution. Through repeating this process the optimal solution will be found.