public void initiateVars() {
GaMain = new singleThreadGAwithProbabilityMatrixPREDA();
Population = new population();
Selection = new binaryTournament();
Crossover = new twoPointCrossover2(); //
Crossover2 = new PMX();
Mutation = new swapMutation();
// Mutation = new swapMutationWithMining2();//swapMutationWithMining2 shiftMutationWithMining2
Mutation2 = new inverseMutation();
ObjectiveFunction = new ObjectiveFunctionScheduleI[numberOfObjs];
ObjectiveFunction[0] =
new ObjectiveEarlinessTardinessPenalty(); // the first objective, tardiness,
// ObjectiveTardiness
// ObjectiveEarlinessTardinessPenalty
Fitness = new singleObjectiveFitness();
objectiveMinimization = new boolean[numberOfObjs];
objectiveMinimization[0] = true;
// objectiveMinimization[1] = true;
encodeType = true;
clone1 = new solutionVectorCloneWithMutation(); // swap mutation
// set schedule data to the objectives
ObjectiveFunction[0].setScheduleData(dueDay, processingTime, numberOfMachines);
// set the data to the GA main program.
GaMain.setLearningRate(lamda, beta); // Interaction Learning rate
GaMain.setWeight(w1, w2); // model=uni*w1 + bi*w2
GaMain.setProbabilityMatrixData(startingGeneration, interval);
GaMain.setSequenceStrategy(strategy);
totalSolnsToExamine = 125000;
DEFAULT_PopSize = 100;
// System.out.println(DEFAULT_PopSize);
// System.exit(0);
DEFAULT_generations = totalSolnsToExamine / (DEFAULT_PopSize);
GaMain.setEvaporationMethod(applyEvaporation, evaporationMethod);
GaMain.setData(
Population,
Selection,
Crossover,
Mutation,
ObjectiveFunction,
Fitness,
DEFAULT_generations,
DEFAULT_PopSize,
DEFAULT_PopSize,
numberOfJob,
DEFAULT_crossoverRate,
DEFAULT_mutationRate,
objectiveMinimization,
numberOfObjs,
encodeType,
elitism);
GaMain.setSecondaryCrossoverOperator(Crossover2, false);
GaMain.setSecondaryMutationOperator(Mutation2, false);
}
