A self-adaptive genetic algorithm with improved mutation mode based on measurement of population diversity
- 69 Downloads
Genetic algorithm (GA) is an important and effective method to solve the optimization problem, which has been widely used in most practical applications. However, the premature convergence of GA has unexpected effect on the algorithm’s performance, the main reason is that the evolution of outstanding individuals multiply rapidly will lead to premature loss of population’s diversity. To solve the above problem, a method to qualify the population diversity and similarity between adjacent generations is proposed. Then, according to the evaluation of population diversity and the fitness of individual, the adaptive adjustment of crossover and mutation probability is realized. The results of several benchmark functions show that the proposed algorithm can search the optimal solution of almost all benchmark functions and effectively maintain the diversity of the population. Compared with the existing algorithms, it has greatly improved the convergence speed and the global optimal solution.
KeywordsGenetic algorithm Mutation mode Population diversity Self-adaptive genetic algorithm
This work is supported by the project of the First-Class University and the First-Class Discipline (No. 10301-017004011501). And we wish to thank the anonymous reviewers who helped to improve the quality of the paper.
Compliance with ethical standards
Conflict of interest
We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.
- 1.Choubey NS, Kharat MU (2011) Approaches for handling premature convergence in CFG induction using GA. Soft Comput Ind Appl 96:55–66Google Scholar
- 2.Potter MA, De Jong KA (1994) A cooperative coevolutionary approach to function optimization. In: Proceedings of the parallel problem solving from Nature-PPSN III, international conference on evolutionary computation. LNCS 866. Springer, Berlin, pp 249–257Google Scholar
- 5.Ramadan SZ (2013) Reducing premature convergence problem in genetic algorithm: application on travel salesman problem. Comput Inf Sci 6(1):47–57Google Scholar
- 15.Uzor CJ, Gongora M, Coupland S, Passow BN (2014) Real-world dynamic optimization using an adaptive-mutation compact genetic algorithm. In: Proceedings of 2014 IEEE symposium on computational intelligence in dynamic and uncertain environments (CIDUE), pp 17–23Google Scholar
- 18.Meng W, Han X, Hong B (2006) Bee evolutionary genetic algorithm. Acta Electron Sin 34(7):1294–1300Google Scholar
- 20.Danoy G, Bouvry P, Martins T (2006) Hlcga: a hybrid competitive coevolutionary genetic algorithm. In: Proceedings of the 6th international conference on hybrid intelligent systems. Computer Society Press, pp 48–51Google Scholar
- 21.Zhou Q, Luo WJ (2010) A novel multi-population genetic algorithm for multiple-choice multidimensional knapsack problems. In: Proceedings of the 5th international symposium on advances in computation and intelligence. Springer, Berlin, pp 148–157Google Scholar
- 24.Campos VEM, Pereira AGC (2013) Modeling the genetic algorithm by a non-homogeneous Markov chain: weak and strong ergodicity. Theory Probab Appl 57(57):185–192Google Scholar
- 27.Jalali Varnamkhasti M, Lee LS, Bakar A (2015) A genetic algorithm with fuzzy crossover operator and probability. Adv Oper Res 2012:1687–9147Google Scholar