scores for individuals in score_results are wrong

[jyotikab]

Here is the code I use:

import numpy as np
import evolutionary_search as ev_se

bound_pos = [0,1]
bound_neg = [-1,0]
data = dict()
for i in xrange(2):
        if i <=0:
                data["param_"+str(i)] = np.arange(bound_pos[0],bound_pos[1], 0.1)
        else:
                data["param_"+str(i)] = np.arange(bound_neg[0],bound_neg[1], 0.1)

def fit(param_0,param_1):
        sum1 = param_0+param_1
        return sum1

best_params, best_score, score_results, hist,log = ev_se.maximize(fit,data,args={},verbose=True,generations_number=100)
============================================
output: Best individual is: {'param_1': -0.1000000000000002, 'param_0': 0.90000000000000002}
with fitness: 0.8
============================================
However, when I look for all individuals with fitness 0.8 (there should be none except the best individual) in score_results:
===========================
print score_results
(({'param_0': 0.90000000000000002, 'param_1': -0.90000000000000002},
  0.79999999999999982),
 ({'param_0': 0.0, 'param_1': -0.20000000000000018}, 0.79999999999999982),
 ({'param_0': 0.90000000000000002, 'param_1': -0.80000000000000004},
  0.79999999999999982),
..........................................
=======================================

This is clearly incorrect , because for the 1st individual : {'param_0': 0.90000000000000002, 'param_1': -0.90000000000000002}, the score should be 0.0