I’m not sure if I’m completely understanding genetic algorithms and how they work, I’m trying to learn via ai4r http://ai4r.rubyforge.org/geneticAlgorithms.html
If in Job Shop Scheduling, which I believe can be solved by GA(?), isn’t cost of any single job is based on how it related to it’s predecessors? I was thinking I would calculate a cost based on the placement of the chromosome with a dynamic score of how well it is placed rather than a binary value, but I’m not sure this works.
Anybody have any experience with this? or does a GA only work when the difference between any two genomes is static?
I hope I have the right terminology here, as I mentioned, I’m just learning.
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I think I’m using a bit of the wrong terminology here. I referred to ‘fitness’ when I think what I actually wanted to use was cost matrix.
The example I’m going from describes this
Each chromosome must represent a posible solution for the problem. This class conatins an array with the list of visited nodes (cities of the tour). The size of the tour is obtained automatically from the traveling costs matrix. You have to assign the costs matrix BEFORE you run the genetic search. The following costs matrix could be used to solve the problem with only 3 cities:
data_set = [ [ 0, 10, 5],
[ 6, 0, 4],
[25, 4, 0]
]
Ai4r::GeneticAlgorithm::Chromosome.set_cost_matrix(data_set)
so in my instance, I’m thinking the ‘cost’ of each chromosome is dynamic based on it’s neighbours.
Since you asked in a comment to make this an answer, I took the liberty of summarizing my earlier responses as well so it’s all in one place. The answer to the specific question of “what is a penalty term” is in item #3 below.
The way a standard genetic algorithm works is that each “chromosome” is a complete solution to the problem. In your case, an ordering for the jobs to be submitted. The confusion, I think, centers around the notion that because the individual contributions to fitness made by a particular job in that schedule varies according to the rest of the schedule, you must need something “dynamic”. That’s not really true. From the point of view of the GA, the only thing that has a fitness is the entire solution. So a dynamic problem is one in which the fitness of a whole schedule can change over time. Going back to the TSP, a dynamic problem would be one in which touring cities in order of A, B, C, D, and then E actually had a different distance each time you tried it. Even though the cost of a tour through B depends on which cities come before and after B in the tour, once you decide that, the costs are static, and because the GA only ever receives costs for entire tours, all it knows is that [A,B,C,D,E] has a constant fitness. No dynamic trickery needed.
Now, your second question was how to handle constraints like, for the TSP example, what if you need to ensure that the salesman gets to Paris by a certain time? Typically, there are three ways to try to handle this.
Never allow a solution to be generated in which he doesn’t get there before 2:00. Sometimes this is easy, other times it’s very hard. For instance, if the constraint was “he cannot start at city X”, it’s fairly easy to just not generate solutions that don’t start with X. Often though, simply finding valid solutions can be hard, and so this approach doesn’t really work.
Allow constraints to be violated, but fix them afterward. In the TSP example, you let crossover and mutation produce any possible tour, but then scan through it to see if he gets to Paris too late. If so, swap the position of Paris with some earlier city in the tour. Again though, sometimes it can be hard to figure out a good way to repair violations.
Penalize the fitness of an infeasible solution. Here, the idea is that even if I can’t prevent him from getting to Paris too late and I can’t fix it if he does, I can at least make the fitness arbitrarily worse. For TSP, the fitness is the length of the tour. So you might say that if a tour gets him to Paris too late, the fitness is the length of the tour + 100. That let’s the solution stay in the population (it might be very good otherwise, so you want it to have a chance to pass on some of its genes), but you make it less likely to be selected, because your selection and replacement methods pick individuals with better fitness values.
For your JSP problem, typically you’re looking to minimize the makespan. The same three options are available to you if you do have some constraints. But from what I can tell, you don’t really have such constraints. I think you’re trying to inject too much knowledge into the process rather than letting the evolutionary algorithm come up with it on its own. That is, you don’t necessarily worry about telling the GA that some arrangements of jobs are better than others. You just assign higher fitness to the better ones and let the process converge.
That said, injecting information like this is often a really good thing to do, but you want to have a good understanding of the basic algorithm first. Let’s say that we know that for TSP, it’s more likely that a good solution will connect cities that are close to one another. The way I would use that information inside a GA would be to generate random solutions non-uniformly (perhaps with a greedy heuristic). I might also replace the standard crossover and mutation algorithms with something customized. Mutation is typically easier to do this with than crossover. To mutate a TSP solution, I might pick two connected cities, break the connection, and then look for a way to reconnect them that was “closer”. That is, if a tour is [A,B,C,D,E,F,G,H], I might pick the edge [B,C] at random, and then look for another edge, maybe [F,G], such that when I connected them crossways to get [A,B,G,D,E,F,C,H], the total tour length was lower. I could even extend that mutation beyond one step — create a loop that keeps trying to break and reconnect edges until it can’t find a shorter tour. This leads to what is usually called a hybrid GA because it’s a GA hybridized with a local search; sometimes also called a Memetic Algorithm. These sorts of algorithms usually outperform a black-box GA because you’re giving the algorithm “hints” to bias it towards trying things you expect to be good.
I think this idea of a memetic algorithm is pretty close to what you were hitting on in your original question of wondering how to deal with the fact that the contribution to fitness from a particular job depends on where the other jobs are in the schedule. The only stumbling block there is that you were a bit unlucky in that the somewhat reasonable idea of thinking of this as “dynamic” leads you a bit astray, as “dynamic” actually means something entirely different here.
So to wrap up, there’s nothing “dynamic” about your problem, so the things people do with GAs for dynamic problems will be entirely unhelpful. A standard GA will work with no fancy tricks. However, the idea of using information you have about what schedules work better can be introduced into the genetic operators, and will probably result in a significantly better overall algorithm.