Evolutionary approach to inductive inference is accomplished by another class of automata called evolutionary inductive Turing machines (Burgin and Eberbach, 2009; 2012). An evolutionary inductive Turing machine is a (possibly infinite) sequence "E" = {"A"["t"]; "t" = 1, 2, 3, ... } of inductive Turing machines "A"["t"] each working on generations Xwhich are coded as words in the alphabet of the machines "A"["t". The goal is to build a “population” "Z" satisfying the inference condition. The automaton "A"["t"] called a component, or a level automaton, of E represents (encodes) a one-level evolutionary algorithm that works with input generations "X"["i"] of the population by applying the variation operators v and selection operator s. The first generation "X"is given as input to "E" and is processed by the automaton "A"[1, which generates/produces the first generation "X"as its transfer output, which goes to the automaton "A"[2. For all "t" = 1, 2, 3, ..., the automaton "A"["t"] receives the generation "X"["t" − 1] as its input from "A"["t" − 1] and then applies the variation operator v and selection operator "s", producing the generation "X"["i" + 1] and sending it to "A"["t" + 1] to continue evolution.