Evolutionary algorithms and their application to optimal control studies -art. no. 023420

Coherent control of a physical or chemical process can be achieved by using phase and amplitude modulated femtosecond laser pulses. A self-learning lo op, which connects a femtosecond pulse shaper, an optimization algorithm, a nd an experimental feedback signal, can automatically steer the interaction between system and electric field and allows control even without any know ledge of the Hamiltonian. The dependability of such a loop is essential to the significance of the optimization results, assigning the optimization al gorithm an important role within these learning loops. In this paper, an ev olutionary strategy is presented in detail that has successfully been appli ed to femtosecond pulse shaping in optimal control experiments. A general i ntroduction to evolutionary algorithms is given and the specific adaptation for femtosecond pulse shaping is described. The stability and effectivenes s of the algorithm is investigated both in experiments and simulations with an emphasis on the influence of steering parameters of the algorithm, numb er of configurations in search space, and noise. The algorithm optimizes a set of variables parametrizing the electric field. This particular mapping greatly facilitates the dissection of the optimization goal which is demons trated by three possible parametrizations and associated applications: poly nomial phase functions and adaptive femtosecond pulse compression, periodic phase functions and control of nonlinear photon transitions, multiple puls e structures and control of molecular dynamics.