Changes between Version 38 and Version 39 of GAIIA

Timestamp:

Jan 11, 2010, 10:10:46 PM (14 years ago)

Author:

mihai.istin

Comment:

--

GAIIA

@@ -30 +30 @@
 
 The input:
- * the graph of tasks that have to be scheduled 
+ * The graph of tasks that have to be scheduled. Each task is represented as a node while the dependencies between tasks are represented as edges. Each tasks (node) has associated a computational cost and each dependency (edge) has an communication costs.  
 [[Image(DAG.jpg)]]
- * the graph of available resources
+ * The processor topology graph which is also described as a DAG. Each node represent a processor (and the weight represents the speed) while each edge represents the communication cost between processors.   
 The output:
- * the scheduling meaning the task-resource associations
+ * The schedule with contains the mapping of tasks on processors and the order of tasks to be executed on the same processor.  
+
 The goal:
  * load balancing
  * minimum makespan
-The algorithm:
+ * minimum idle times 
+
+The proposed algorithm
+    Our solution is based on a hybrid evolutionary algorithm with combines the benefits of genetic and immune algorithms algorithms in order to provide efficient solutions. It is known that the convergence time of genetic algorithms is highly influenced by the average fitness of the initial population. So if we could find a way to provide a population with an good average fitness at the initialization phase of the genetic algorithm, the algorithm will converge faster. 
+   Immune algorithms are also bio-inspired algorithms that are based on the human immune system model. They evolve a population of antibodies in order to fit better the antigens that are threatening the immune system. 
+   As a computational model, immune algorithms can be designed to evolve a population of chromosomes representing the antibodies in order to have a good fitness. The implementation of such algorithms is based on the clonal selection principle which model the response of an immune system to an intruder. Basically, the current population of antibodies is evaluated and the best individuals (individuals with the best fitness) are selected for the maturation process. During this process, for each selected individual will be made a number of clones proportionally with its fitness. Then each clone will suffer multiple mutations. The number of mutations will be inverse proportionally with its fitness. Then, the clones are evaluated, and the best are selected in order to survive to the next generation. The antibodies with the lowest fitness will be removed from the current population. 
+   The main advantage of immune algorithms is that they are capable of evolving a population with a good average fitness after only several iteration. On the other hand  
  * uses immune algorithms
 [[Image(ia.jpg)]]