Above are papers related to the following paper: [Paper_ID] Paper #1134865. 	[Title] DISTRIBUTED GENETIC ALGORITHMS FOR PARTITIONING UNIFORM GRIDS [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1120643. 	[Title] Genetic Algorithms as Multi-Coordinators in Large-Scale Optimization [Abstract] We present high-level, decomposition-based algorithms for large-scale block-angular optimization problems containing integer variables, and demonstrate their effectiveness in the solution of large-scale graph partitioning problems. These algorithms combine the subproblem-coordination paradigm (and lower bounds) of price-directive decomposition methods with knapsack and genetic approaches to the utilization of "building blocks" of partial solutions. Even for graph partitioning problems requiring billions of variables in a standard 0-1 formulation, this approach produces high-quality solutions (as measured by deviations from an easily computed lower bound), and substantially outperforms widely-used graph partitioning techniques based on heuristics and spectral methods. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #100935. 	[Title] Distribution Category:  Users Guide to the PGAPack Parallel Genetic Algorithm Library [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #423816. 	[Title] Minimum-Perimeter Domain Assignment [Abstract] For certain classes of problems defined over two-dimensional domains with grid structure, optimization problems involving the assignment of grid cells to processors present a nonlinear network model for the problem of partitioning tasks among processors so as to minimize interprocessor communication. Minimizing interprocessor communication in this context is shown to be equivalent to tiling the domain so as to minimize total tile perimeter, where each tile corresponds to the collection of tasks assigned to some processor. A tight lower bound on the perimeter of a tile as a function of its area is developed. We then show how to generate minimum-perimeter tiles. By using assignments corresponding to near-rectangular minimum-perimeter tiles, closed form solutions are developed for certain classes of domains. We conclude with computational results with parallel high-level genetic algorithms that have produced good (and sometimes provably optimal) solutions for very large perimeter minimization problems. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #48781. 	[Title] Optimal and Asymptotically Optimal Equi-partition of Rectangular Domains via Stripe Decomposition [Abstract] We present an efficient method for assigning any number of processors to tasks associated with the cells of a rectangular uniform grid. Load balancing equi-partition constraints are observed while approximately minimizing the total perimeter of the partition, which corresponds to the amount of interprocessor communication. This method is based upon decomposition of the grid into stripes of "optimal" height. We prove that under some mild assumptions, as the problem size grows large in all parameters, the error bound associated with this feasible solution approaches zero. We also present computational results from a high level parallel Genetic Algorithm that utilizes this method, and make comparisons with other methods. On a network of workstations, our algorithm solves within minutes instances of the problem that would require one billion binary variables in a Quadratic Assignment formulation. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #97390. 	[Title] On the Virtues of Parameterized Uniform Crossover [Abstract] Traditionally, genetic algorithms have relied upon 1 and 2-point crossover operators. Many recent empirical studies, however, have shown the benefits of higher numbers of crossover points. Some of the most intriguing recent work has focused on uniform crossover, which involves on the average L/2 crossover points for strings of length L. Theoretical results suggest that, from the view of hyperplane sampling disruption, uniform crossover has few redeeming features. However, a growing body of experimental evidence suggests otherwise. In this paper, we attempt to reconcile these opposing views of uniform crossover and present a framework for understanding its virtues. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #397488. 	[Title] Fast EquiPartitioning of Rectangular Domains using Stripe Decomposition [Abstract] This paper presents a fast algorithm that provides optimal or near optimal solutions to the minimum perimeter problem on a rectangular grid. The minimum perimeter problem is to partition a grid of size M N into P equal area regions while minimizing the total perimeter of the regions. The approach taken here is to divide the grid into stripes that can be filled completely with an integer number of regions . This striping method gives rise to a knapsack integer program that can be efficiently solved by existing codes. The solution of the knapsack problem is then used to generate the grid region assignments. An implementation of the algorithm partitioned a 1000 1000 grid into 1000 regions to a provably optimal solution in less than one second. With sufficient memory to hold the M N grid array, extremely large minimum perimeter problems can be solved easily. Please summarise all the given information.

Paper #1134865 has the title `DISTRIBUTED GENETIC ALGORITHMS FOR PARTITIONING UNIFORM GRIDS  
` and the abstract `nan`.
Question: Which category should Paper #1134865 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #1134865 has the title `DISTRIBUTED GENETIC ALGORITHMS FOR PARTITIONING UNIFORM GRIDS  
` and the abstract `nan`.
Question: Which category should Paper #1134865 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