Journal Article10.1023/A:1006529012972
Genetic Algorithms for the Travelling Salesman Problem: A Review of Representations and Operators
TL;DR: This paper presents crossover and mutation operators, developed to tackle the Travelling Salesman Problem with Genetic Algorithms with different representations such as: binary representation, path representation, adjacency representation, ordinal representation and matrix representation.
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Abstract: This paper is the result of a literature study carried out by the authors. It is a review of the different attempts made to solve the Travelling Salesman Problem with Genetic Algorithms. We present crossover and mutation operators, developed to tackle the Travelling Salesman Problem with Genetic Algorithms with different representations such as: binary representation, path representation, adjacency representation, ordinal representation and matrix representation. Likewise, we show the experimental results obtained with different standard examples using combination of crossover and mutation operators in relation with path representation.
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Citations
Evolutionary computation for automatic Web service composition: an indirect representation approach
TL;DR: A novel framework based on an indirect representation is proposed and tested, with results showing that the quality of the solutions produced by the proposed indirect approach is higher than that of a baseline direct representation-based approach for twelve out of the thirteen datasets considered.
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Naturally selecting solutions: The use of genetic algorithms in bioinformatics
TL;DR: An overview of genetic algorithms is provided and some of the most recent applications of this approach to bioinformatics based problems are surveyed.
GeneRepair - A Repair Operator for Genetic Algorithms
George G. Mitchell,Diarmuid O'Donoghue,David Barnes,Mark McCarville +3 more
- 01 Jan 2003
TL;DR: Using GeneRepair along side traditional corsover and mutation operators, this operator has been able to travers the search space of a problem and generate very good results in an extremely efficent manner, in both time and number of evaluations required.
A multi-objective evolutionary algorithm based on decomposition and constraint programming for the multi-objective team orienteering problem with time windows
TL;DR: A multi-objective evolutionary algorithm based on decomposition and constraint programming (CPMOEA/D) is developed to solve the MOTOPTW, where checkpoints with multiple profits are considered and the results are compared with the best-known solutions from the literature.
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The Attribute Based Hill Climber
TL;DR: Results of applying the Attribute Based Hill Climber algorithm to two classical optimisation problems, the Travelling Salesman Problem and the Quadratic Assignment Problem, show it to be competitive with existing general purpose heuristics in these areas.
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