2010
DOI: 10.5120/1536-139
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A Novel Encoding Scheme for Traveling Tournament Problem using Genetic Algorithm

Abstract: Traveling Tournament Problem is a sports timetabling problem that abstracts the important issues in creating timetables where team travel is an important issue. The instances of this problem seem to be very difficult to solve even for very small cases. In this paper, Author has suggested a Novel encoding scheme for representing a solution instance. The scheme is implemented and tested for several instances of Traveling tournament problem such

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Cited by 9 publications
(5 citation statements)
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“…The genetic algorithm has genetic operators, including crossover and mutation rates, which operate on the chromosome [32]. For a given chromosome that obtains by the encoding scheme, it is also required to know which permutation will correspond to [33]. This form of encoding allows genes that would help them to be constructed.…”
Section: A Novel Coding Proceduresmentioning
confidence: 99%
“…The genetic algorithm has genetic operators, including crossover and mutation rates, which operate on the chromosome [32]. For a given chromosome that obtains by the encoding scheme, it is also required to know which permutation will correspond to [33]. This form of encoding allows genes that would help them to be constructed.…”
Section: A Novel Coding Proceduresmentioning
confidence: 99%
“…It has been treated by numerous heuristics-based approaches, such as simulated annealing [9], hybrid algorithms [18], genetic algorithms [5], populationbased simulated annealing [17], hyperheuristics [10], ant colonybased approaches [4] and artificial immune algorithms [11] among others. An input for the TTP consists in t teams (t even) and a t × t symmetric matrix d, such that dij represents the distance between the homes of teams Ti and Tj.…”
Section: Traveling Tournament Problemsmentioning
confidence: 99%
“…Luca Di Gaspero and Schaerf, proposed a tabu search approach for TTP with several neighborhood structures [8]. Nitin and Choubey proposed an evolutionary approach for TTP [13]. Anagnostopoulos et al provided a simulated algorithm that explores feasible and infeasible schedules using several structures of neighborhoods and complex movements [2].…”
Section: Introductionmentioning
confidence: 98%