e0e90bab32976898a01bb2da29c3002b83596425,GA.py,,GA,#,334

Before Change

    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__+"\"")  

After Change

    if not isinstance(lb, list):
        lb = [lb] * dim
    if not isinstance(ub, list):
        ub = [ub] * dim
        
    bestIndividual=numpy.zeros(dim)    
    scores=numpy.random.uniform(0.0, 1.0, popSize) 
    bestScore=float("inf")
    
    ga = numpy.zeros((popSize, dim))
    for i in range(dim):
        ga[:, i]=numpy.random.uniform(0,1,popSize) * (ub[i] - lb[i]) + lb[i]
    convergence_curve=numpy.zeros(iters)
    
    print("GA is optimizing  \""+objf.__name__+"\"")  

Instances