On permutation representations for scheduling problems

Bierwirth, Christian; Mattfeld, Dirk C.; Kopfer, Herbert

doi:10.1007/3-540-61723-x_995

Cited by 140 publications

(92 citation statements)

References 3 publications

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“…For yet other problems, crossover must retain general pairwise element precedences, and not simply edges, in order to produce children phenotypically similar to the parents. Precedence Preservative Crossover (PPX) [3] is designed to do this, while others that may work well for such problems include NWOX, UOBX, and CX. There are yet other crossover operators that introduce problem-dependent knowledge into crossover (e.g., [28,37,9]).…”

Section: Ga Permutation Operatorsmentioning

confidence: 99%

Genetic Algorithm Parameter Control: Application to Scheduling with Sequence-Dependent Setups

Cicirello¹

2016

Proceedings of the 9th EAI International Conference on Bio-Inspired Information and Communications Technologies (Formerly BIONE

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Genetic algorithms, and other forms of evolutionary computation, are controlled by numerous parameters, such as crossover and mutation rates, population size, among others depending upon the specific form of evolutionary computation as well as which operators are employed. Setting the values for these parameters is no simple task. In this paper, we develop a genetic algorithm with adaptive control parameters for an NP-Hard scheduling problem known as weighted tardiness scheduling with sequence-dependent setups. Our genetic algorithm uses the permutation representation along with the non-wrapping order crossover and insertion mutation operators. We encode the control parameters within the members of the population and evolve these during search using Gaussian mutation. We demonstrate this approach out-performs a manually tuned genetic algorithm for the problem, and that it converges upon effective parameter values very early in the run.

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Section: Ga Permutation Operatorsmentioning

confidence: 99%

Genetic Algorithm Parameter Control: Application to Scheduling with Sequence-Dependent Setups

Cicirello¹

2016

Proceedings of the 9th EAI International Conference on Bio-Inspired Information and Communications Technologies (Formerly BIONE

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“…In this work, we consider only the direct constraints during the crossover. Therefore, the conceived crossover promotes the relative order positions such as the PPX crossover (Bierwirth et al, 1996). The proposed crossover operator is designated Indirect Precedence Constraint Crossover (IPCX) and can be described in the two following steps : (i) all individuals in the current population are considered and the indirect precedence constraints between jobs concurrently in all the individuals are kept, as shown in Figure 3; and (ii) the CBS approach tries to solve the problem while adding the indirect precedence constraints built in the previous step and an upper bound consisting of the objective function value of the best parent.…”

Section: The Collaborative Hybrid Genetic Algorithmmentioning

confidence: 99%

Hybrid Genetic Algorithms for the Single Machine Scheduling Problem with Sequence-Dependent Setup Times

Sioud¹,

MarcGravel²,

Gagné³

2012

Real-World Applications of Genetic Algorithms

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“…The Precedence Preservative Crossover(PPX) is presented by [3]. First the offspring chromosome is initialized empty.…”

Section: A Constraint Crossover Operator -Ippxmentioning

confidence: 99%

Genetic algorithms for large join query optimization

Dong

Liang

2007

Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation

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Genetic algorithms (GAs) have long been used for large join query optimization (LJQO). Previous work takes all queries as based on one granularity to optimize GAs and compares their efficiency with other query optimization algorithms. However, we believe that large join queries are based on a granularity that is too large (1) to optimize GAs and (2) to compare the efficiency of different randomized optimization algorithms. Besides, while previous work only discusses the efficiency of basic GAs for LJQO, we believe that hybrid GAs reduce search space to improve GAs efficiency.We will present a genetic optimization model which includes factors affecting the efficiency of GAs. In this model, the query model is the granularity upon which GAs are optimized. Based on six typical query models, experiments have been done, first, to optimize four classes of GAs; and second, to prove the rationality of the query model as a trade-off between the efficiency and robustness of GAs. Finally, we will provide suggestions for choosing one of four classes of GAs and for the settings and combinations of components of GAs.

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On permutation representations for scheduling problems

Cited by 140 publications

References 3 publications

Genetic Algorithm Parameter Control: Application to Scheduling with Sequence-Dependent Setups

Genetic Algorithm Parameter Control: Application to Scheduling with Sequence-Dependent Setups

Hybrid Genetic Algorithms for the Single Machine Scheduling Problem with Sequence-Dependent Setup Times

Genetic algorithms for large join query optimization

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