Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Mutation is one of the most important stages of the genetic algorithm because of its impact on the exploration of global optima, and to overcome premature convergence. There are many types of mutation, and the problem lies in selection of the appropriate type, where the decision becomes more difficult and needs more trial and error.  investigates the use of more than one mutation operator to enhance the performance of genetic algorithms. Novel mutation operators are proposed, in addition to two selection strategies for the mutation operators, one of which is based on selecting the best mutation operator and the other randomly selects any operator. Several experiments on some Travelling Salesman Problems (TSP) re conducted to evaluate the proposed methods, and these re compared to the ll-known exchange mutation and rearrangement mutation. The results show the importance of some of the proposed methods, in addition to the significant enhancement of the genetic algorithm's performance, particularly when using more than one mutation operator.
['Ahmad B. A. Hassanat', "Esra'a Alkafaween", 'Nedal Alnawaiseh', 'Mohammad Ali Abbadi', 'Mouhammd Alkasassbeh', 'Mahmoud B. Alhasanat']
I am writing a research paper on a idea Enhancing Genetic Algorithms using Multi Mutations
[{"from": "human", "value": "I am writing a research paper on a idea Enhancing Genetic Algorithms using Multi Mutations"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Mutation is one of the most important stages of the genetic algorithm because of its impact on the exploration of global optima, and to overcome premature convergence. There are many types of mutation, and the problem lies in selection of the appropriate type, where the decision becomes more difficult and needs more trial and error.  investigates the use of more than one mutation operator to enhance the performance of genetic algorithms. Novel mutation operators are proposed, in addition to two selection strategies for the mutation operators, one of which is based on selecting the best mutation operator and the other randomly selects any operator. Several experiments on some Travelling Salesman Problems (TSP) re conducted to evaluate the proposed methods, and these re compared to the ll-known exchange mutation and rearrangement mutation. The results show the importance of some of the proposed methods, in addition to the significant enhancement of the genetic algorithm's performance, particularly when using more than one mutation operator."}]