Changes between Version 46 and Version 47 of GAIIA

Timestamp:

Jan 12, 2010, 7:58:31 PM (14 years ago)

Author:

mihai.istin

Comment:

--

GAIIA

@@ -78 +78 @@
 The crossover operator for a chromosome representation used follows the following steps:
 
-a. a cut position is randomly selected ( in the above example the cut point is chosen between genes four and five) and for each chromosome results a head and a tail segment;
+i. a cut position is randomly selected ( in the above example the cut point is chosen between genes four and five) and for each chromosome results a head and a tail segment;
 
-b. the first offspring is obtained by joining the head segment of the first parent with the combination of the tail segments both parents; more specific, for each gene the task is taken from the first parent and the assigned task is taken from the parent;
+ii. the first offspring is obtained by joining the head segment of the first parent with the combination of the tail segments both parents; more specific, for each gene the task is taken from the first parent and the assigned task is taken from the parent;
 
-c. the second offspring is obtain the same way, using the head segment of the first parent and the combination of tail segments of both parents, but this time, the tasks are taken from the second parent while the assigned processors are taken from the first parent
+iii. the second offspring is obtain the same way, using the head segment of the first parent and the combination of tail segments of both parents, but this time, the tasks are taken from the second parent while the assigned processors are taken from the first parent
 
 b ''Simple Gene Mutation''
@@ -97 +97 @@
 
 
+'''3. Fitness function''' 
+
+The fitness function is an essential element in a GA. It gives an appreciation of the quality of a potential solution according to the problem’s specification. For the scheduling problem, the goal is to obtain task assignments that ensure minimum execution time, maximum processor utilization, a well balanced load across all machines and last but not least to ensure that the precedence of the task is not violated. According to the chromosome encoding and genetic operators presented previously all individuals respect the task DAG, so the focus should be on the other goals of the problem.
+
+For the fitness function we will use the following formula:
+
+[[Image(form3.jpg)]]
+
+
+'''4. Selection Method '''
+
+As a selection method we use a tournament selection algorithms. Basically this algorithm has two steps. First a given number of chromosomes are randomly selected from the population. Next, the selected individual with the best fitness is chosen to reproduce through crossover.   
 
 ==  The Immune Algorithms ==