Impact of Partial Separability on Large-Scale Optimization
Computational Optimization and Applications
Completely Derandomized Self-Adaptation in Evolution Strategies
Evolutionary Computation
Adaptive Encoding: How to Render Search Coordinate System Invariant
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
A Simple Modification in CMA-ES Achieving Linear Time and Space Complexity
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Comparing results of 31 algorithms from the black-box optimization benchmarking BBOB-2009
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Comparison-based optimizers need comparison-based surrogates
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Local meta-models for optimization using evolution strategies
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Investigating the local-meta-model CMA-ES for large population sizes
EvoApplicatons'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part I
| Hi-index | 0.00 |
In this paper, we propose a new variant of the covariance matrix adaptation evolution strategy with local meta-models (lmm-CMA) for optimizing partially separable functions. We propose to exploit partial separability by building at each iteration a meta-model for each element function (or sub-function) using a full quadratic local model. After introducing the approach we present some first experiments using element functions with dimensions 2 and 4. Our results demonstrate that, as expected, exploiting partial separability leads to an important speedup compared to the standard CMA-ES. We show on the tested functions that the speedup increases with increasing dimensions for a fixed dimension of the element function. On the standard Rosenbrock function the maximum speedup of λ is reached in dimension 40 using element functions of dimension 2. We show also that higher speedups can be achieved by increasing the population size. The choice of the number of points used to build the meta-model is also described and the computational cost is discussed.