A Stable and Efficient Algorithm for the Rank-One Modification of the Symmetric Eigenproblem
SIAM Journal on Matrix Analysis and Applications
Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Learning probability distributions in continuous evolutionary algorithms– a comparative review
Natural Computing: an international journal
Completely Derandomized Self-Adaptation in Evolution Strategies
Evolutionary Computation
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
pCMALib: a parallel fortran 90 library for the evolution strategy with covariance matrix adaptation
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
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A limited memory version of the covariance matrix adaptation evolution strategy (CMA-ES) is presented. This algorithm, L-CMA-ES, improves the space and time complexity of the CMA-ES algorithm. The L-CMA-ES uses the $m$ eigenvectors and eigenvalues spanning the m-dimensional dominant subspace of the n-dimensional covariance matrix, C, describing the mutation distribution. The algorithm avoids explicit computation and storage of $C$ resulting in space and time savings. The L-CMA-ES algorithm has a space complexity of \mathcal{O}(nm) and a time complexity of \mathcal{O}(nm^2). The algorithm is evaluated on a number of standard test functions. The results show that while the number of objective function evaluations needed to find a solution is often increased by using m the increase in computational efficiency leads to a lower overall run time.