A Fast and Efficient Genetic Evolution Algorithm

Yu-Cheng Liu,Yu-Bin Liu

doi:10.19026/rjaset.5.4440

Abstract

This study presents an improved genetic algorithm. The algorithm introduced acceleration operator in the traditional genetic algorithm, effectively reducing the computational complexity. The search speed of the algorithm has been greatly improved, so that it can quickly find the global optimal solution. The accelerating collaborative operator lessons from the thoughts of binary search algorithm combining with the variable step length strategy. The accelerating operator has strong local search ability and crossover and mutation operators have strong global search ability, then combining these operators generates a new Genetic algorithm. The tests on the different functions show that the improved algorithm has the advantages of faster convergence and higher stability in the case of a small population than traditional genetic algorithm and can effectively avoid the premature phenomenon.

Highlights

As a bionic algorithm in the macro sense
Because it does not depend on the specific problem areas and has good robustness, it is widely used in many disciplines.With further research, the genetic algorithm showed many deficiencies, such as premature convergence, easy to fall into local optimum, the slow search speed and strong dependence on the initial population
Because of it’s insufficient and inspire of the imitation of human intelligence (HSIC), People have been proposed many improvements algorithm and the new intelligent algorithms, such as the parallel genetic algorithm based on fixed-point theory (Chen et al, 2010), adaptive genetic algorithm, super-selection strategy genetic algorithm, chaos genetic algorithm, ant colony algorithm, PSO algorithm, simulated annealing algorithm, immune algorithm (Gong et al, 2008) coevolutionary algorithm and so on

Summary

Introduction

As a bionic algorithm in the macro sense. Genetic Algorithm (GA) inspires a good structure by simulating the Darwinian “survival of the fittest, survival of the fittest” principle. For the genetic algorithm introduced the acceleration operator, we applied random initialization and taken the population size to be 50, 50 individuals were carried out random single-point crossover and random single-point mutation, the document storages the optimal individual. For the traditional genetic algorithm, we applied random initialization and taken the population size to be 200, 200 individuals were carried out random single-point crossover with having the crossover rate of 25% and random single-point mutation with having the mutation rate of 5%, the championship selection, the document storages the optimal individual.

Results

Conclusion