Learning Based Genetic Algorithm for Task Graph Scheduling

Algorithm 1

A new task graph scheduling algorithm.

Step 1:

 - Create the initial population (classic chromosomes) as described in Section 3.1.

Step 2:

 - While termination condition has not been satisfied Do

   - For each classic chromosome in the current population Do

      - Convert it to the extended structure chromosome (such as Table 2).

      - Enhance chromosome by applying learning operators (reward and penalty) on the chromosome until the

       makespan could be less, described in Section 3.4.

   - Convert extended chromosome to classic chromosome by removing rows related depth and probability

   - Apply the standard roulette wheel selection strategy for selecting potentially useful individuals for recombination

   - Apply the standard two-point crossover operator on processors in two enhanced chromosomes

   - Apply the standard mutation operator on processors in two enhanced chromosomes

   - Apply the reuse idle time heuristic on 30% of best chromosomes, described in Section 3.3.