diff --git a/generators/nb_gm_004.py b/generators/nb_gm_004.py
index bbac64f..5c0d43f 100644
--- a/generators/nb_gm_004.py
+++ b/generators/nb_gm_004.py
@@ -3,21 +3,31 @@
 
 
 class SequenceGenerator:
-    """nb_gm_003
-        Generates a sequence of trials with even distribution of stimuli.
-    """
+    """Generate a sequence progressively according to a predefined TL ratio and an even distribution"""
 
-    def __init__(self, choices, trials, n=3, targets_ratio=0.33):
-        self.trials, self.choices, self.n, self.targets_ratio = trials, choices, n, targets_ratio
-        self.seq = []
+    def __init__(self, choices, trials, tl=4.0, n=3, targets_ratio=0.33):
+        """Initialize the genetic algorithm optimizer for n-back sequences.
+        :param choices:
+        :param trials:
+        :param tl:
+        :param n:
+        """
+        self.tl, self.trials, self.choices, self.n, self.targets_ratio = tl, trials, choices, n, targets_ratio
+        self.sequence = list()
         self.norm_even_dist = scipy.stats.norm(0, trials/2)
         self.norm_targets_ratio_dist = scipy.stats.norm(targets_ratio, 0.5)
+        self.norm_tl_ratio_dist = scipy.stats.norm(tl, trials/2)
 
     def generate(self):
-        print('next')
-        while not self.seq or len(self.seq) < self.trials:
-            self.seq = self.__find_best_next_sequence(self.seq, self.choices)
-        return self.seq
+        while not self.sequence or len(self.sequence) < self.trials:
+            self.sequence = self.__find_best_next_sequence(self.sequence, self.choices)
+        return self.sequence
+
+    def next_trial(self):
+        if self.sequence and len(self.sequence) >= self.trials:
+            return None
+        self.sequence = self.__find_best_next_sequence(self.sequence, self.choices)
+        return self.sequence[-1]
 
     def __find_best_next_sequence(self, seq: list, choices: list) -> list:
         import sys
@@ -26,20 +36,24 @@
         random.shuffle(choices)  # to avoid ordering effect
         for choice in choices:
             tmp_seq = seq + list(choice)
-            cost = self.__cost(tmp_seq)
+            cost = self.cost(tmp_seq)
             if cost < min_cost:
                 min_cost = cost
                 best_seq = tmp_seq
         return best_seq
 
-    def __even_distribution_cost(self, seq):
+    def calc_even_distribution_distance(self, seq):
+        """
+        Calculate fitness according to the similarity to the desired uniform distribution.
+        :param seq: a string
+        :return:
+        """
         costs = {c: 0.0 for c in self.choices}
         for c in list(seq):
             costs[c] += (1.0 if costs.__contains__(c) else 0.0)
         even_ratio = self.trials / len(self.choices)
         costs = {k: abs(v - even_ratio)/self.trials for k, v in costs.items()}
-        max_cost = max(list(costs.values()))
-        return 1.0 - self.norm_even_dist.pdf(max_cost)
+        return max(list(costs.values()))
 
     def cost(self, seq):
         """
@@ -77,14 +91,15 @@
 
 def __generate_stat_csv(filename):
     alphabetic_choices = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H']
-    trials = 24
+    trials = 64
     n = 2
     import csv
     import heapq
     with open(filename, mode='w') as stat_dist_file:
         writer = csv.writer(stat_dist_file, delimiter=',', quotechar='"', quoting=csv.QUOTE_MINIMAL)
         writer.writerow(['index'] + alphabetic_choices + ['ralph_skewed'])
-        for i in range(100):
+        for i in range(10):
+            print(f'generating sequence {i}...')
             generator = SequenceGenerator(alphabetic_choices, n=n, trials=trials)
             seq = generator.generate()
             dist = [float(seq.count(c)) for c in alphabetic_choices]
@@ -93,4 +108,4 @@
 
 
 if __name__ == '__main__':
-    __generate_stat_csv('../stat/nb_gm_003_2back_24trials.csv')
+    __generate_stat_csv('../stat/progressive_random_2back_24trials.csv')