GLOBAL OPTIMIZATION, SEARCHING AND MACHINE LEARNING METHOD BASED ON LAMARCK ACQUIRED GENETIC PRINCIPLE

摘要

A global optimization, searching and machine learning method based on the Lamarck acquired genetic principle, comprising: step 1: constructing an objective function f(x) according to a problem object; step 2: encoding the problem object into a chromosome of a genetic algorithm, automatically calculating or inputting operation parameters, and initializing same; step 3: performing iteration and optimization on a current (k-th generation) population Gk={P1k, P2k,...Psk} by means of a Lamarck "acquired genetic operator" and "a use and disuse operator" of the invention according to an evaluation of the objective function f(x); and step 4: inputting an optimal solution set of the problem object. In the method, combining the natural laws of "acquired genetics" and "use and disuse" of Lamarck's theory of evolution with modern "epigenetics" and the natural law of "survival of the fittest" of Darwin's theory of evolution simplifies the structure of genetic algorithms, overcomes multiple technical defects of existing algorithms, and improves the efficiency, global optimality, and sustainability of later evolution thereof, so as to better solve more problems regarding global optimization, searching and machine learning.