Ruggedness and neutrality—the NKp family of fitness landscapes
ALIFE Proceedings of the sixth international conference on Artificial life
Properties of fitness functions and search landscapes
Theoretical aspects of evolutionary computing
Fitness Distance Correlation as a Measure of Problem Difficulty for Genetic Algorithms
Proceedings of the 6th International Conference on Genetic Algorithms
A comparison of predictive measures of problem difficulty inevolutionary algorithms
IEEE Transactions on Evolutionary Computation
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Closed-loop evolutionary multiobjective optimization
IEEE Computational Intelligence Magazine
Optimisation of CDMA-based mobile telephone networks: algorithmic studies on real-world networks
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
| Hi-index | 0.00 |
Many evolutionary algorithm applications involve either fitness functions with high time complexity or large dimensionality (hence very many fitness evaluations will typically be needed) or both. In such circumstances, there is a dire need to tune various features of the algorithm well so that performance and time savings are optimized. However, these are precisely the circumstances in which prior tuning is very costly in time and resources. There is hence a need for methods which enable fast prior tuning in such cases. We describe a candidate technique for this purpose, in which we model a landscape as a finite state machine, inferred from preliminary sampling runs. In prior algorithm-tuning trials, we can replace the 'real' landscape with the model, enabling extremely fast tuning, saving far more time than was required to infer the model. Preliminary results indicate much promise, though much work needs to be done to establish various aspects of the conditions under which it can be most beneficially used. A main limitation of the method as described here is a restriction to mutation-only algorithms, but there are various ways to address this and other limitations.