GLOBAL OPTIMIZATION, SEARCH AND MACHINE LEARNING METHOD BASED ON THE LAMARCKIAN PRINCIPLE OF INHERITANCE OF ACQUIRED CHARACTERISTICS

摘要

The invention discloses a global optimization, search and machine learning method based on the Lamarckian principle of inheritance of acquired characteristics, comprising step 1: constructing an objective function ƒ(P) according to the problem being solved, where P represents a set of candidate solutions to the problem; step 2: encoding P into a genetic algorithm (GA) chromosome, inputting or automatically calculating algorithmic parameters of the GA, and initializing the algorithm and the population of candidate solution generation G₀={P₀¹, P₀², . . . , P₀^S}, where S is the size of the population G and 0 stands for the initial generation; step 3: at generation k, optimizing the prevailing population of the candidate solutions G_k={P_k¹, P_k², . . . , P_k^S} iteratively using a Lamarckian “Heredity Operator” and a “Use-and-Disuse Operator” based on the values of ƒ(G_k); and step 4: outputting the final set of optimal solutions to the problem.