4678e49d41959e8c66e656c3b4fca59e2dd11d1f,GA.py,,GA,#,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

        
        scores = calculateCost(objf, ga, PopSize, lb, ub)
            
        bestScore = min(scores)
        
        //Sort from best to worst
        ga, scores = sortPopulation(ga, scores)

Instances