Examples of aima.core.search.local.GeneticAlgorithm

aima.core.search.local.GeneticAlgorithm

Artificial Intelligence A Modern Approach (3rd Edition): Figure 4.8, page 129.

 function GENETIC-ALGORITHM(population, FITNESS-FN) returns an individual inputs: population, a set of individuals FITNESS-FN, a function that measures the fitness of an individual repeat new_population <- empty set for i = 1 to SIZE(population) do x <- RANDOM-SELECTION(population, FITNESS-FN) y <- RANDOM-SELECTION(population, FITNESS-FN) child <- REPRODUCE(x, y) if (small random probability) then child <- MUTATE(child) add child to new_population population <- new_population until some individual is fit enough, or enough time has elapsed return the best individual in population, according to FITNESS-FN -------------------------------------------------------------------------------- function REPRODUCE(x, y) returns an individual inputs: x, y, parent individuals n <- LENGTH(x); c <- random number from 1 to n return APPEND(SUBSTRING(x, 1, c), SUBSTRING(y, c+1, n))

Figure 4.8 A genetic algorithm. The algorithm is the same as the one diagrammed in Figure 4.6, with one variation: in this more popular version, each mating of two parents produces only one offspring, not two. @author Ciaran O'Reilly @author Mike Stampone @param < A> the type of the alphabet used in the representation of the individuals in the population (this is to provide flexibility in terms of how a problem can be encoded).

      for (int i = 0; i < 20; i++) {
        population.add(fitnessFunction
            .generateRandomIndividual(boardSize));
      }


      GeneticAlgorithm ga = new GeneticAlgorithm(boardSize,
          fitnessFunction.getFiniteAlphabetForBoardOfSize(boardSize),
          0.15);


      // Run for a set number of iterations
      String bestIndividual = ga.geneticAlgorithm(population,
          fitnessFunction, 100);


      System.out.println("Iterations Best Individual=\n"
          + fitnessFunction.getBoardForIndividual(bestIndividual));
      System.out.println("Fitness="
          + fitnessFunction.getValue(bestIndividual));
      System.out.println("Is Goal="
          + fitnessFunction.isGoalState(bestIndividual));
      System.out.println("Itertions=" + ga.getIterations());


      // Run till goal is achieved
      bestIndividual = ga.geneticAlgorithm(population, fitnessFunction,
          fitnessFunction);


      System.out.println("Goal Test Best Individual=\n"
          + fitnessFunction.getBoardForIndividual(bestIndividual));
      System.out.println("Fitness="
          + fitnessFunction.getValue(bestIndividual));
      System.out.println("Is Goal="
          + fitnessFunction.isGoalState(bestIndividual));
      System.out.println("Itertions=" + ga.getIterations());


    } catch (Exception e) {
      e.printStackTrace();
    }
  }

Examples of aima.core.search.local.GeneticAlgorithm

Related Classes of aima.core.search.local.GeneticAlgorithm