genetic-algorithm

This is in response to an answer given in this question: How to create a good evaluation function for a game? , particularly by @David (it is the first answer). Background : I am using a genetic algorithm to optimize the hyper parameters in a game playing agent that is using minimax / alpha beta pruning (with iterative deepening). In particular, I would like to optimize the heuristic (evaluation) function parameters using a genetic algorithm. The evaluation function I use is: f(w) = w * num_my_moves - (1-w) * num_opponent_moves The only parameter to optimize is w in [0,1]. Here's how I

I need to create ordered crossover in matlab. I have parents P1 and P2 as follow: P1=[1 2 3 4 ; 0 1 1 0], P2=[3 2 1 4 ; 0 1 0 0]. First 1 [at place P1(2,2) and P2(2,2)] is my crossover point. now I need to offsprings as follow: O1=[1 2 3 4 ; 0 1 0 0], O2=[3 2 1 4 ; 0 1 0 0]. Can you please help me? Best, Elnaz To find the crossover point, use a logical AND operator on the second line of the parents: idx = find(P1(2, :) & P2(2, :)); Then we create the offsprings by switching values between parents after the crossover point: O1 = [P1(:, 1:idx), P2(:, idx + 1:end)]; O2 = [P2(:, 1:idx), P1(:, idx

Is IT possible to use DEAP ( http://deap.readthedocs.io/en/master/ ) with a spark cluster to map the fitness evaluation function. I would like to run a GA but the fitness function is rather long and I was planning on distributing it on a spark cluster. You should look at the Using Multiple Processors section in the DEAP documentation and at this example . They explain how to replace the map function in the DEAP toolbox by a map function of your choice. To use pyspark to map the fitness evaluation function, you could do something like that: from pyspark import SparkContext sc = SparkContext

I am writing a genetic algorithm. My population quickly develops a monoculture. I am using a small population (32 individuals) with a small number of discrete genes (24 genes per individual) and a single point cross-over mating approach. Combine that with a roulette wheel selection strategy and it is easy to see how all the genetic diversity is lost in just a few dozen generations. What I would like to know is, what is the appropriate response? I do not have academic-level knowledge on GAs and only a few solutions come to mind: Use a larger population. (slow) Use runtime checks to prevent in

I have a question regarding Genetic Programming. I am going to work on a genetic algorithm for a game called Battleships . My question is: How would I decide upon a "decision" model for the AI to evolve? And how does that work? I have read multiple papers and multiple answers that just speak about using different models, but could not find something specific, which, unfortunately, I apparently need to wrap my head around the problem. I want it to evolve over multiple iterations and "learn" what works best, but not sure how to save these "decisions" (I know to a file, but "encoded" how?) in a

My question is linked with this one: Roulette-wheel selection in Genetic algorithm. Population needs to be sorted first? If we don't sort the population what is the way of organizing roulette wheel selection for it? Surely, we have to search in linear way now. Have you got any code snippets in C++ or Java for this case? The population does not need to be sorted at all - the key to roulette selection is that the probability of a given individual being selected for reproduction is proportional to its fitness. Say you have an unsorted population, with fitnesses as follows: [12, 45, 76, 32, 54, 21