Solving Very Large Optimization Problems (Up to One Billion Variables) with a Parallel Evolutionary Algorithm in CPU and GPU.
Santiago IturriagaSergio NesmachnowPublished in: 3PGCIC (2012)
Keyphrases
- evolutionary algorithm
- optimization problems
- graphics processing units
- combinatorial optimization
- nonlinear programming
- parallel implementation
- multi objective
- general purpose
- parallel processing
- parallel computing
- parallel computation
- gpu implementation
- parallel programming
- differential evolution
- convex optimization problems
- metaheuristic
- multi objective optimization
- multi threaded
- computing systems
- graphics processors
- fitness function
- simulated annealing
- multithreading
- pc cluster
- cpu implementation
- quadratic programming problems
- heterogeneous computing
- massively parallel
- real time
- mutation operator
- traveling salesman problem
- memory bandwidth
- genetic algorithm
- cluster of workstations
- graphics hardware
- constrained optimization problems
- multi core processors
- shared memory
- level parallelism
- optimization methods
- efficient implementation
- function optimization
- nsga ii
- constraint problems
- variable ordering
- cost function
- parallel hardware
- graphic processing unit
- nonlinear optimization problems
- compute unified device architecture
- computer architecture
- data transfer
- crossover operator
- knapsack problem
- parallel algorithm
- ant colony optimization
- linear programming
- neural network