A Generalized Markov-Chain Modelling Approach to $(1,λ)$-ES Linear Optimization: Technical Report
This is an incremental theoretical extension for evolutionary strategies in optimization, relevant to researchers in computational optimization.
The paper tackles the problem of generalizing Markov-chain modeling for a (1,λ)-ES in linear optimization by relaxing the normality assumption for random steps, providing sufficient conditions for success with constant step-size on a linear function with a linear constraint.
Several recent publications investigated Markov-chain modelling of linear optimization by a $(1,λ)$-ES, considering both unconstrained and linearly constrained optimization, and both constant and varying step size. All of them assume normality of the involved random steps, and while this is consistent with a black-box scenario, information on the function to be optimized (e.g. separability) may be exploited by the use of another distribution. The objective of our contribution is to complement previous studies realized with normal steps, and to give sufficient conditions on the distribution of the random steps for the success of a constant step-size $(1,λ)$-ES on the simple problem of a linear function with a linear constraint. The decomposition of a multidimensional distribution into its marginals and the copula combining them is applied to the new distributional assumptions, particular attention being paid to distributions with Archimedean copulas.