Abstract—Genetic algorithm (GA) is too dependent on the initial population and a lack of local search ability. In this paper, an improved greedy genetic algorithm (IGAA) is proposed to overcome the above-mentioned limitations. This novel type of greedy genetic algorithm is based on the base point, which can generate good initial population, and combine with hybrid algorithms to get the optimal solution. The proposed algorithm is tested with the Traveling Salesman Problem (TSP), and the experimental results demonstrate that the proposed algorithm is a feasible and effective algorithm in solving complex optimization problems
This paper presents the results of an analysis of three algorithms for the Travelling Salesman Probl...
Evolutionary and genetic algorithms are problem-solving methods designed according to a nature inspi...
Genetic Algorithms (GAs) are an evolutionary technique that uses the operators like mutation, crosso...
The Genetic Algorithm (GA) is one of the most important methods used to solve many combinatorial opt...
Traditional genetic algorithms often meet the occurrence of slow convergence and enclosure competiti...
The Traveling salesman problem (TSP) is proved to be NP-complete in most cases. The genetic algorith...
Robust known the exceedingly famed NP-hard problem in combinatorial optimization is the Traveling Sa...
The conventional GA combined with a local search algorithm, such as the 2-OPT, forms a hybrid geneti...
Genetic Algorithms are finding increasing number of applications in a variety of problems in a whole...
Abstract — This paper presents the literature survey review of Travelling Salesman Problem (TSP). TS...
Abstract The performance of Genetic Algorithms (GA) is affected by various factors such as parameter...
TSP is a challenging and popular problem from combinatorial optimization. TSP is often tackled with ...
For the solution of combinatorial problem, various algorithms has been investigated. However, almost...
The generalized traveling salesman problem (GTSP) deals with finding the minimum-cost tour in a clus...
We briefly review previous attempts to generate near-optimal solutions of the Traveling Salesman Pro...
This paper presents the results of an analysis of three algorithms for the Travelling Salesman Probl...
Evolutionary and genetic algorithms are problem-solving methods designed according to a nature inspi...
Genetic Algorithms (GAs) are an evolutionary technique that uses the operators like mutation, crosso...
The Genetic Algorithm (GA) is one of the most important methods used to solve many combinatorial opt...
Traditional genetic algorithms often meet the occurrence of slow convergence and enclosure competiti...
The Traveling salesman problem (TSP) is proved to be NP-complete in most cases. The genetic algorith...
Robust known the exceedingly famed NP-hard problem in combinatorial optimization is the Traveling Sa...
The conventional GA combined with a local search algorithm, such as the 2-OPT, forms a hybrid geneti...
Genetic Algorithms are finding increasing number of applications in a variety of problems in a whole...
Abstract — This paper presents the literature survey review of Travelling Salesman Problem (TSP). TS...
Abstract The performance of Genetic Algorithms (GA) is affected by various factors such as parameter...
TSP is a challenging and popular problem from combinatorial optimization. TSP is often tackled with ...
For the solution of combinatorial problem, various algorithms has been investigated. However, almost...
The generalized traveling salesman problem (GTSP) deals with finding the minimum-cost tour in a clus...
We briefly review previous attempts to generate near-optimal solutions of the Traveling Salesman Pro...
This paper presents the results of an analysis of three algorithms for the Travelling Salesman Probl...
Evolutionary and genetic algorithms are problem-solving methods designed according to a nature inspi...
Genetic Algorithms (GAs) are an evolutionary technique that uses the operators like mutation, crosso...