In this paper it is explained how to solve a fully connected N-City travelling salesman problem (TSP) using a genetic algorithm. A crossover operator to use in the simulation of a genetic algorithm (GA) with DNA is presented. The aim of the paper is to follow the path of creating a new computational model based on DNA molecules and genetic operations. This paper solves the problem of exponentially size algorithms in DNA computing by using biological methods and techniques. After individual encoding and fitness evaluation, a protocol of the next step in a GA, crossover, is needed. This paper also shows how to make the GA faster via different populations of possible solutions
This paper includes a flexible method for solving the travelling salesman problem using genetic algo...
This paper presents the results of an analysis of three algorithms for the Travelling Salesman Probl...
Genetic algorithms, DNA computing, and in vitro evolution are briefly discussed. Elements of these a...
* This work has been partially supported by Spanish Project TIC2003-9319-c03-03 “Neural Networks and...
This thesis discuss about Genetic Algorithm to solve PCB component placement modeled as Travelling S...
Genetic algorithm is one of the possible ways to break the limit of brute-force method in DNA comput...
Genetic Algorithms (GAs) are an evolutionary technique that uses the operators like mutation, crosso...
In this paper we propose a model of encoding data into DNA strands so that this data can be used in ...
For the solution of combinatorial problem, various algorithms has been investigated. However, almost...
In this paper, we apply a genetic algorithm to TSP. Since in TSP, a tour must pass through edges in ...
In this paper, software was developed to solve the travelling salesman problem. The Travelling Sales...
Abstract — This paper presents the literature survey review of Travelling Salesman Problem (TSP). TS...
Abstract: Traveling salesman problem is quite known in the field of combinatorial optimization. Thro...
Genetic Algorithm (GA) is a metaheuristic used in solving combinatorial optimization problems. Inspi...
This paper presents an application of genetic algorithms (GAs) to a well-known traveling salesman pr...
This paper includes a flexible method for solving the travelling salesman problem using genetic algo...
This paper presents the results of an analysis of three algorithms for the Travelling Salesman Probl...
Genetic algorithms, DNA computing, and in vitro evolution are briefly discussed. Elements of these a...
* This work has been partially supported by Spanish Project TIC2003-9319-c03-03 “Neural Networks and...
This thesis discuss about Genetic Algorithm to solve PCB component placement modeled as Travelling S...
Genetic algorithm is one of the possible ways to break the limit of brute-force method in DNA comput...
Genetic Algorithms (GAs) are an evolutionary technique that uses the operators like mutation, crosso...
In this paper we propose a model of encoding data into DNA strands so that this data can be used in ...
For the solution of combinatorial problem, various algorithms has been investigated. However, almost...
In this paper, we apply a genetic algorithm to TSP. Since in TSP, a tour must pass through edges in ...
In this paper, software was developed to solve the travelling salesman problem. The Travelling Sales...
Abstract — This paper presents the literature survey review of Travelling Salesman Problem (TSP). TS...
Abstract: Traveling salesman problem is quite known in the field of combinatorial optimization. Thro...
Genetic Algorithm (GA) is a metaheuristic used in solving combinatorial optimization problems. Inspi...
This paper presents an application of genetic algorithms (GAs) to a well-known traveling salesman pr...
This paper includes a flexible method for solving the travelling salesman problem using genetic algo...
This paper presents the results of an analysis of three algorithms for the Travelling Salesman Probl...
Genetic algorithms, DNA computing, and in vitro evolution are briefly discussed. Elements of these a...