  An interactive stochastics, evaluated by an entropy functional (EF) of a
random field and informational process' path functional (IPF), allows us
modeling the evolutionary information processes and revealing regularities of
evolution dynamics. Conventional Shannon's information measure evaluates a
sequence of the process' static events for each information state and do not
reveal hidden dynamic connections between these events. The paper formulates
the mathematical forms of the information regularities, based on a minimax
variation principle (VP) for IPF, applied to the evolution's both random
microprocesses and dynamic macroprocesses. The paper shows that the VP single
form of the mathematical law leads to the following evolutionary regularities:
-creation of the order from stochastics through the evolutionary macrodynamics,
described by a gradient of dynamic potential, evolutionary speed and the
evolutionary conditions of a fitness and diversity; -the evolutionary hierarchy
with growing information values and potential adaptation; -the adaptive
self-controls and a self-organization with a mechanism of copying to a genetic
code. This law and the regularities determine unified functional informational
mechanisms of evolution dynamics. By introducing both objective and subjective
information observers, we consider the observers' information acquisition,
interactive cognitive evolution dynamics, and neurodynamics, based on the
EF-IPF approach. An evolution improvement consists of the subjective observer s
ability to attract and encode information whose value progressively increases.
The specific properties of a common information structure of evolution
processes are identifiable for each particular object-organism by collecting a
behavioral data from these organisms.
