Mutation Function

[11Johan11]

Takes a solution and mutates it, For our permutation based solutions we can choose from

• Swap Mutation - two positions in the gene are selected at random and their alelle values randomly swapped
• Insert Mutation - Two alleles are selected at random and then "pushed" together like magnets the others making space for them
• Scramble Mutation - Here the entire chromosome, or a subset gets randomly choosen and have their positions scrambled.
• Inversion Mutation - Randomly selects two positions and reversing the order in wich values appear.

For Genetic Algorithms it's always about striking a balance between exploration and exploitation, Exploration (looking through the search space and finding a optimum) Exploitation (looking through closely at a optimum trying to reach the peak)

Exploration is usually very important in the beginning where diversity is key

We could implement all of them and experiment & evaluate.

[11Johan11]

Something we could experiment in later stages if we have time is to have "Self-Adaptive" behaviour where we include the mutation parameters in the solution itself, so in the beginning it will automatically explore heavy(mutate aggressively) but in later stages exploit heavy(mutate very little)