Changes between Version 49 and Version 50 of GAIIA
 Timestamp:
 Jan 13, 2010, 6:02:27 PM (14 years ago)
Legend:
 Unmodified
 Added
 Removed
 Modified

GAIIA
v49 v50 134 134 As can be seen in the above figure, most of the functions of the proposed algorithm have significant execution time (10 to 50%) and can be easily parallelized because they are applied independently to each chromosome from the population. In the next paragraphs we will explain what each function does and how it is better to be parallelized. 135 135 136 First we will present the parallel version for the genetic algorithm and then we will present the aspects related to the immune algorithm parallelization. 137 138 ''1.1 The Parallel Genetic Algorithm'' 139 136 140 The presented analysis reveals that the most time consuming part of the algorithm is the evaluation (or the computation of fitness for each individual). If we analyze the complexity of this phase of the algorithm we will obtain a O(N*M), where N is the number of nodes, M is the maximum number of predecessor that a node can have. More, this function is applied on each of the C chromosomes leading to a total complexity equals to O(C*N*P). If we also consider that this function is applied on each generation, the total complexity is O(G*C*N*M). 137 141 … … 144 148 Another interesting approach is used for the selection algorithm. Here the parallelization is not strait forward. The population of chromosomes will be divided in a number of chunks equal to the number of threads. At each step (of the total TOUR_SIZE steps), each thread will randomly choose an unselected individual from a single chunk. The chunks assignment to threads is done in a round robin manner. After each thread has completed the tour, the individuals with the best fitness is selected to survive in the next generation and the process is started over. 145 149 146 147 * The Parallel Execution Model 150 [[Image(tour.jpg)]] 151 152 ''1.2 The Parallel Immune Algorithm'' 153 154 First of all we have to mention that for the evaluation of the chromosome we use the same approach presented for the genetic algorithm. 155 156 The selection method for the immune algorithm is a simple selection of the best individuals and it is based on choosing the first individuals after they have been sorted. So we will need a parallel sort algorithm. The chosen algorithm is a hybrid algorithm between Quick Sort and Merge Sort. First the population is divided into a number of chunks equal to the number of threads and each thread will sort its chunk using the Quick Sort algorithm (first figure). In the second phase each two distinct chunks will be merged until we will obtain a sorted vector (second figure). 157 158 [[Image(sort1.jpg)]] 159 160 [[Image(sort2.jpg)]] 161 162 In order to produce the population of clones we have first to determine the number of clones corresponding to each individual. Then we have to distribute the cloning process in order to assure load balancing among threads. For this we use a parallel algorithm to compute the partial sums. The same approach will also be used for the mutation process. 163 164 165 ''1.3 The Parallel Execution Model'' 166 148 167 149 168 [[Image(execp.jpg)]]