How to force dependency / linkage of genes in a genetic algorithm?

Question

For a current project, I want to use genetic algorithms - currently I had a look at the jenetics library.

How can I force that some genes are dependent on each other? I want to map CSS on the gene, f.e. I have genes indicating if an image is displayed, and in case it is also the respective height and width. So I want to have those genes as a group togheter, as it would make no sense that after a crossover, the chrosome would indicate something like "no image" - height 100px - width 0px.

Is there a method to do so? Or maybe another library (in java) which supports this?

Many thanks!

Answer 1

You want to embed more knowledge into your system to reduce the search space.

If it would be knowledge about the structure of the solution, I would propose taking a look at grammatical evolution (GE). Your knowledge appears to be more about valid combinations of codons, so GE is not easily applicable.

It might be possible to combine a few features into a single codon, but this may be undesirable and/or unfeasible (e.g. due to great number of possible combinations).

But in fact you don't have an issue here:

• it's fine to have meaningless genotypes — they will be removed due to the selection pressure
• it's fine to have meaningless codon sequences — it's called "bloat"; bloat is quite common to some evolutionary algorithms (usually discussed in the context of genetic programming) and is not strictly bad; fighting with bloat too much can reduce the search performance

Answer 2

If you know how your genome is encoded - that is, you know which sequences of chromosomes form groups - then you could extend (since you mention jenetics) io.jenetics.MultiPointCrossover to avoid splitting groups. (Source code available on GitHub.)

It could be as simple as storing ranges of genes which form groups if one of the random cut indexes would split a group, adjusting the index to the nearest end of the group. (Of course this would cause a statistically higher likelihood of cuts at the ends of groups; it would probably be better to generate a new random location until it doesn't intersect a group.)

But it's also valid (as Pete notes) to have genes which aren't meaningful (ignored) based on other genes; if the combination is anti-survival it will be selected out.