1. Genetic Algorithms Contents 1. Basic Concepts 2. Algorithm
3. Practical considerations

2. individuals surviving from the previous generation
Basic Concepts
Simulated Annealing Tabu Search
versus
Genetic Algorithms
a single solution is carried over from one iteration to the next
population based method
Individuals (or members of population or chromosomes)
individuals surviving from the previous generation +
children
generation

3. Fitness of an individual (a schedule) is measured by the value of the associated objective function
Representation
Example from scheduling problems:
the order of jobs to be processed can be represented as a permutation: [1, 2, ... ,n]
Initialisation
How to choose initial individuals?
High-quality solutions obtained from another heuristic technique can help a genetic algorithm to find better solutions more quickly than it can from a random start.

4. Reproduction
Crossover: combine the sequence of operations on one machine in one parent schedule with a sequence of operations on another machine in another parent.
Example 1. Ordinary crossover operator is not useful!
Cut Point
P1 = [ ]
P2 = [ ]
O1 = [ ]
O2 = [ ]
Example 2. Partially Mapped Crossover
Cut Point 1
Cut Point 2
31
42
55
P1 = [ ]
P2 = [ ]
O1 = [ ]
O2 = [ ]

5. Example 3. Preserves the absolute positions of the jobs taken from P1 and the relative positions of those from P2
Cut Point 1
P1 = [ ]
P2 = [ ]
O1 = [ ]
O2 = [ ]
Example 4. Similar to Example 3 but with 2 crossover points.
Cut Point 1
Cut Point 2
P1 = [ ]
P2 = [ ]
O1 = [ ]

6. Mutation enables genetic algorithm to explore the search space
Mutation enables genetic algorithm to explore the search space not reachable by the crossover operator.
Adjacent pairwise interchange in the sequence
[1,2, ... ,n]
[2,1, ... ,n]
Exchange mutation: the interchange of two randomly chosen elements of the permutation
Shift mutation: the movement of a randomly chosen element a random number of places to the left or right
Scramble sublist mutation: choose two points on the string in random and randomly permuting the elements between these two positions.

7. Selection
Roulette wheel: the size of each slice corresponds to the fitness of the appropriate individual.
slice for the 1st individual
slice for the 2nd individual
selected individual .
Steps for the roulette wheel
1. Sum the fitnesses of all the population members, TF
2. Generate a random number m, between 0 and TF
3. Return the first population member whose fitness added to the preceding population members is greater than or equal to m

8. Tournament selection
1. Randomly choose a group of T individuals from the population.
2. Select the best one.
How to guarantee that the best member of a population will survive?
Elitist model: the best member of the current population is set to be a member of the next.

9. Algorithm
Step 1.
k=1
Select N initial schedules S1,1 ,... , S1,N using some heuristic
Evaluate each individual of the population
Step 2.
Create new individuals by mating individuals in the current population using crossover and mutation
Delete members of the existing population to make place for the new members
Evaluate the new members and insert them into the population
Sk+1,1 ,... , Sk+1,N
Step 3.
k = k+1
If stopping condition = true then return the best individual as the solution and STOP
else go to Step 2

10. Example
Metric: minimize total tardiness (tardiness of a job is the amount by
which it exceeds its deadline)
Population size: 3
Selection: in each generation the single most fit individual reproduces using adjacent pairwise interchange chosen at random there are 4 possible children, each is chosen with probability 1/4 Duplication of children is permitted. Children can duplicate other members of the population.
Initial population: random permutation sequences

11. Generation 1
Individual
Cost
Selected individual: with offspring 13245, cost 20
Generation 2
Individual
Cost
Average fitness is improved, diversity is preserved
Selected individual: with offspring 12354, cost 17
Generation 3
Individual
Cost
Selected individual: with offspring 12435, cost 11

12. Generation 4
Individual
Cost
Selected individual: 12435
This is an optimal solution.
Disadvantages of this algorithm:
Since only the most fit member is allowed to reproduce (or be mutated) the same member will continue to reproduce unless replaced by a superior child.

13. Practical considerations
Population size: small population run the risk of seriously under-covering the solution space, while large populations will require computational resources. Empirical results suggest that population sizes around 30 are adequate in many cases, but are more common.
Mutation is usually employed with a very low probability.