Journal of Computer and System Sciences
On classes of functions for which No Free Lunch results hold
Information Processing Letters
Spatially Structured Evolutionary Algorithms: Artificial Evolution in Space and Time (Natural Computing Series)
Encyclopedia of Optimization
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Parallelism and evolutionary algorithms
IEEE Transactions on Evolutionary Computation
A Quantum-Inspired Evolutionary Algorithm Based on P systems for Knapsack Problem
Fundamenta Informaticae
Letters: A variant of P systems for optimization
Neurocomputing
Membrane computing as a modeling framework: cellular systems case studies
SFM'08 Proceedings of the Formal methods for the design of computer, communication, and software systems 8th international conference on Formal methods for computational systems biology
A membrane algorithm for the min storage problem
WMC'06 Proceedings of the 7th international conference on Membrane Computing
An improved membrane algorithm for solving time-frequency atom decomposition
WMC'09 Proceedings of the 10th international conference on Membrane Computing
A Quantum-Inspired Evolutionary Algorithm Based on P systems for Knapsack Problem
Fundamenta Informaticae
Biological plausibility in optimisation: an ecosystemic view
International Journal of Bio-Inspired Computation
| Hi-index | 0.00 |
A new type of approximate algorithms for optimization problems, called membrane algorithms, is proposed, which can be seen as an application of membrane computing to evolutionary computing. A membrane algorithm consists of several membrane separated regions, where subalgorithms and tentative solutions to the optimization problem to be solved are placed, as well as a solution transporting mechanism between adjacent regions. The subalgorithms improve tentative solutions simultaneously. After that, the best and worst solutions in a region are sent to adjacent inner and outer regions, respectively. By repeating this process, a good solution will appear in the innermost region. The algorithm terminates if a terminate condition is satisfied. A simple condition of this type is the number of iterations, while a little more sophisticated condition becomes true if the good solution is not changed during a predetermined period. Computer experiments show that such algorithms are rather efficient in solving the travelling salesman problem.