Linear analysis of genetic algorithms
Theoretical Computer Science
Simple genetic algorithm with local tuning: efficient global optimizing technique
Journal of Optimization Theory and Applications
An effective hybrid optimization strategy for job-shop scheduling problems
Computers and Operations Research
Effects of Symbiotic Evolution in Genetic Algorithms for Job-Shop Scheduling
HICSS '01 Proceedings of the 34th Annual Hawaii International Conference on System Sciences ( HICSS-34)-Volume 3 - Volume 3
Local Search Genetic Algorithms for the Job Shop Scheduling Problem
Applied Intelligence
Computers and Industrial Engineering - Special issue: Selected papers from the 30th international conference on computers; industrial engineering
IEEE Transactions on Neural Networks
Scheduling and control modeling of HVLV systems using max-plus algebra
VECoS'11 Proceedings of the Fifth international conference on Verification and Evaluation of Computer and Communication Systems
Static and adaptive mutation techniques for genetic algorithm: a systematic comparative analysis
International Journal of Computational Science and Engineering
Impact of static and adaptive mutation techniques on the performance of Genetic Algorithm
International Journal of Hybrid Intelligent Systems
Hi-index | 12.05 |
An improved adaptive genetic algorithm (IAGA) for solving the minimum makespan problem of job-shop scheduling problem (JSP) is presented. Though the traditional genetic algorithm (GA) exhibits implicit parallelism and can retain useful redundant information about what is learned from previous searches by its representation in individuals in the population, yet GA may lose solutions and substructures due to the disruptive effects of genetic operators and is not easy to regulate GA's convergence. The proposed IAGA is inspired from hormone modulation mechanism, and then the adaptive crossover probability and adaptive mutation probability are designed. The proposed IAGA is characterized by simplifying operations, high search precision, overcoming premature phenomenon and slow evolution. The proposed method by employing operation-based encoding is effectively applied to solve a dynamic job-shop scheduling problem (DJSP) and a complicated contrastive experiment of JSP in manufacturing system. Meanwhile, in order to ensure to create a feasible solution, a new method for crossover operation is adopted, named, partheno-genetic operation (PGO). The computational results validate the effectiveness of the proposed IAGA, which can not only find optimal or close-to-optimal solutions but can also obtain both better and more robust results than the existing genetic algorithms reported recently in the literature. By employing IAGA, machines can be used more efficiently, which means that tasks can be allocated appropriately, production efficiency can be improved, and the production cycle can be shortened efficiently.