A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Controlled Elitist Non-dominated Sorting Genetic Algorithms for Better Convergence
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
Constrained Test Problems for Multi-objective Evolutionary Optimization
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
A software framework for efficient system-level performance evaluation of embedded systems
Proceedings of the 2003 ACM symposium on Applied computing
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Multiobjective optimization problems with complicated Pareto sets, MOEA/D and NSGA-II
IEEE Transactions on Evolutionary Computation
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Performance assessment of multiobjective optimizers: an analysis and review
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
LION'05 Proceedings of the 5th international conference on Learning and Intelligent Optimization
| Hi-index | 0.00 |
We present a multi-objective genetic algorithm called magnifying front genetic algorithm (MFGA) designed in order to treat complex real-world optimisation problems. A first source of complexity is the presence of different input variables classes (real, discrete and categorical). MFGA is able to treat appropriately each of them as well as any combination. Moreover, real-world applications often require a long time to evaluate objective values from input variables. We deal with this issue working on elitism (in order to tune properly the balance between explorative and exploitative capabilities of the algorithm) and introducing a parallel steady-state evolution scheme, which is able to use the available computing resources as much intensively as possible. We test the algorithm on two different scenarios: mathematical benchmarks and real-world applications. For the latter one we chose a problem arising in multi-processor system-on-chip (MPSoC) design, a field which is characterised by discrete and more often categorical variables.