4678e49d41959e8c66e656c3b4fca59e2dd11d1f,GA.py,,GA,#Any#Any#Any#Any#Any#Any#,265

Before Change

    for l in range(iters):
        
        //Loop through chromosomes in population
        for i in range(0,PopSize):
            // Return back the search agents that go beyond the boundaries of the search space
            ga[i,:]=numpy.clip(ga[i,:], lb, ub)

            // Calculate objective function for each search agent
            fitness=objf(ga[i,:])     
            
            scores[i] = fitness
                
            if(bestScore>fitness):
                bestScore=fitness
                bestIndividual=numpy.copy(ga[i,:])
        
        //Apply evolutionary operators to chromosomes
        ga = runOperators(ga, scores, bestIndividual, bestScore, cp, mp, PopSize, lb, ub) 
                    
        convergence_curve[l]=bestScore
     

After Change

    s=solution()
        
    bestIndividual=numpy.zeros(dim)    
    scores=numpy.random.uniform(0.0, 1.0, PopSize) 
    bestScore=float("inf")
    
    ga=numpy.random.uniform(0,1,(PopSize,dim)) *(ub-lb)+lb
    convergence_curve=numpy.zeros(iters)
    
    print("GA is optimizing  \""+objf.__name__+"\"")  
    
    timerStart=time.time() 
    s.startTime=time.strftime("%Y-%m-%d-%H-%M-%S")
    
    for l in range(iters):

        //crossover
        ga = crossoverPopulaton(ga, scores, PopSize, cp, keep)
           
        //mutation
        mutatePopulaton(ga, PopSize, mp, keep, lb, ub)
           
        ga = clearDups(ga, lb, ub)
        
        scores = calculateCost(objf, ga, PopSize, lb, ub)
            
        bestScore = min(scores)
        

Instances