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

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 f ( 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 G 0 ={ P 0 1 , P 0 2 , ..., P 0 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 k 1 , P k 2 , ..., P k S } iteratively using a Lamarckian "Heredity Operator" and a "Use-and-Disuse Operator" based on the values of f ( G k ); and step 4: outputting the final set of optimal solutions to the problem.