Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Job Shop Scheduling with Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
Direct Chromosome Representation and Advanced Genetic Operators for Production Scheduling
Proceedings of the 5th International Conference on Genetic Algorithms
An Efficient Genetic Algorithm for Job Shop Scheduling Problems
Proceedings of the 6th International Conference on Genetic Algorithms
Parameter-Free Genetic Algorithm Inspired by ``Disparity Theory of Evolution''
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
A Comparison of Genetic Algorithms for the Static Job Shop Scheduling Problem
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
On Permutation Representations for Scheduling Problems
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Production scheduling and rescheduling with genetic algorithms
Evolutionary Computation
Evolutionary Scheduling: A Review
Genetic Programming and Evolvable Machines
Performance evaluation of a parameter-free genetic algorithm for job-shop scheduling problems
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
| Hi-index | 0.01 |
We propose a new genetic algorithm (GA) for job-shop scheduling problems (JSSP) based on the parameter-free GA (PfGA) and parallel distributed PfGA proposed by Sawai et al. The PfGA is not only simple and robust, but also does not need to set almost any genetic parameters in advance that need to be set in other GAs. The performance of PfGA is high for functional optimization problems of 5- or 10-dimensions, but its performance for combinatorial optimization problems, which search space is larger than the functional optimization, has not been investigated. We propose a new algorithm for JSSP based on an extended PfGA, extended to real-coded version. The GA uses random keys for representing permutation of jobs. Simulation results show that the proposed GA can attain high quality solutions for typical benchmark problems without parameter tuning.