Above are papers related to the following paper: [Paper_ID] Paper #1128208. 	[Title] Genetic Algorithm based Scheduling in a Dynamic Manufacturing Environment [Abstract] The application of adaptive optimization strategies to scheduling in manufacturing systems has recently become a research topic of broad interest. Population based approaches to scheduling predominantly treat static data models, whereas real-world scheduling tends to be a dynamic problem. This paper briefly outlines the application of a genetic algorithm to the dynamic job shop problem arising in production scheduling. First we sketch a genetic algorithm which can handle release times of jobs. In a second step a preceding simulation method is used to improve the performance of the algorithm. Finally the job shop is regarded as a nondeterministic optimization problem arising from the occurrence of job releases. Temporal Decomposition leads to a scheduling control that interweaves both simulation in time and genetic search. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #69284. 	[Title] An Overview of Genetic Algorithms Part 1, Fundamentals [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #58758. 	[Title] A Generalized Permutation Approach to Job Shop Scheduling with Genetic Algorithms [Abstract] In order to sequence the tasks of a job shop problem (JSP) on a number of machines related to the technological machine order of jobs, a new representation technique mathematically known as "permutation with repetition" is presented. The main advantage of this single chromosome representation is in analogy to the permutation scheme of the traveling salesman problem (TSP) that it cannot produce illegal sets of operation sequences (infeasible symbolic solutions). As a consequence of the representation scheme a new crossover operator preserving the initial scheme structure of permutations with repetition will be sketched. Its behavior is similar to the well known Order-Crossover for simple permutation schemes. Actually the GOX operator for permutations with repetition arises from a Generalisation of OX. Computational experiments show, that GOX passes the information from a couple of parent solutions efficiently to offspring solutions. Together, the new representation and GOX support the cooperative aspect of the genetic search for scheduling problems strongly. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #33895. 	[Title] A Promising genetic Algorithm Approach to Job-Shop Scheduling, Rescheduling, and Open-Shop Scheduling Problems [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1128204. 	[Title] The Application of a Parallel Genetic Algorithm to the n=m=P=C max Flowshop Problem [Abstract] Hard combinatorial problems in sequencing and scheduling led recently into further research of genetic algorithms. Canonical coding of the symmetric TSP can be modified into a coding of the n-job m-machine flowshop problem, which configurates the solution space in a different way. We show that well known genetic operators act intelligently on this coding scheme. They implecitely prefer a subset of solutions which contain the probably best solutions with respect to an objective. We conjecture that every new problem needs a determination of this necessary condition for a genetic algorithm to work, i. e. a proof by experiment. We implemented an asynchronous parallel genetic algorithm on a UNIX-based computer network. Computational results of the new heuristic are discussed. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #576973. 	[Title] Control of Parallel Population Dynamics by Social-Like Behavior of GA-Individuals [Abstract] A frequently observed difficulty in the application of genetic algorithms to the domain of optimization arises from premature convergence. In order to preserve genotype diversity we develop a new model of auto-adaptive behavior for individuals. In this model a population member is an active individual that assumes social-like behavior patterns. Different individuals living in the same population can assume different patterns. By moving in a hierarchy of "social states" individuals change their behavior. Changes of social state are controlled by arguments of plausibility. These arguments are implemented as a rule set for a massively-parallel genetic algorithm. Computational experiments on 12 large-scale job shop benchmark problems show that the results of the new approach dominate the ordinary genetic algorithm significantly. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1688. 	[Title] Using Neural Networks and Genetic Algorithms as Heuristics for NP-Complete Problems [Abstract] Paradigms for using neural networks (NNs) and genetic algorithms (GAs) to heuristically solve boolean satisfiability (SAT) problems are presented. Since SAT is NP-Complete, any other NP-Complete problem can be transformed into an equivalent SAT problem in polynomial time, and solved via either paradigm. This technique is illustrated for hamiltonian circuit (HC) problems. Please summarise all the given information.

Paper #1128208 has the title `Genetic Algorithm based Scheduling in a Dynamic Manufacturing Environment  
` and the abstract `The application of adaptive optimization strategies to scheduling in manufacturing systems has recently become a research topic of broad interest. Population based approaches to scheduling predominantly treat static data models, whereas real-world scheduling tends to be a dynamic problem. This paper briefly outlines the application of a genetic algorithm to the dynamic job shop problem arising in production scheduling. First we sketch a genetic algorithm which can handle release times of jobs. In a second step a preceding simulation method is used to improve the performance of the algorithm. Finally the job shop is regarded as a nondeterministic optimization problem arising from the occurrence of job releases. Temporal Decomposition leads to a scheduling control that interweaves both simulation in time and genetic search.
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Question: Which category should Paper #1128208 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #1128208 has the title `Genetic Algorithm based Scheduling in a Dynamic Manufacturing Environment  
` and the abstract `The application of adaptive optimization strategies to scheduling in manufacturing systems has recently become a research topic of broad interest. Population based approaches to scheduling predominantly treat static data models, whereas real-world scheduling tends to be a dynamic problem. This paper briefly outlines the application of a genetic algorithm to the dynamic job shop problem arising in production scheduling. First we sketch a genetic algorithm which can handle release times of jobs. In a second step a preceding simulation method is used to improve the performance of the algorithm. Finally the job shop is regarded as a nondeterministic optimization problem arising from the occurrence of job releases. Temporal Decomposition leads to a scheduling control that interweaves both simulation in time and genetic search.
`.
Question: Which category should Paper #1128208 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].

This paper should be classified into: Genetic_Algorithms