I am implementing a genetic algorithm to use as an optimisation algorithm to evolve robots. The robots have certain parameters (represented as floats) which can lie anywhere within a certain range defined for each parameter. My goal is to optimise these parameters to produce the fittest robot.
There are about 25 parameters to optimise over, all of which are >= 0. A generation consists of 32 robots and a typical evolutionary run may have 6000 generations, so that is 192000 robots in total.
One problem I was noticing was that many times, the same robot was being produced by the random mutation function and its fitness was re-evaluated. I want to try and minimise this from happening as the physics simulation engine has a large time expense.
It would be impractical (especially from a memory point of view) to remember all robot parameters that have ever been produced and use a brute force solution.
One solution I thought of was to use a hash function to and a large array of booleans, and everytime a mutation occurs,
largeArray[hash(robotParameters)] is checked to see if it is set. If it is, the mutation occurs again to produce a different set of parameters, and if it isn't,
largeArray[hash(robotParameters)] is then set.
What is a good hash function implementation that would work for me? Preferably, there would already be an implementation in Python or C++.