Evolutionary strategies of optimization

Evolutionary algorithms have proved to be a powerful tool for solving complex optimization problems. The underlying physical and biological strategies can equally be described by a Schr\"odinger equation. The properties of the dynamics of optimization are encoded in the spectrum of the Hamiltonian. Analytic solutions and convergence velocity of the dynamics are calculated and compared with simulations of the corresponding algorithms. The connection between physical and biological strategies is analyzed. Mixing both strategies creates a basic class of evolutionary algorithms improving robustness and velocity of optimization.