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 #395075. 	[Title] EVOLVING NEURAL NETWORKS WITH COLLABORATIVE SPECIES [Abstract] We present a coevolutionary architecture for solving decomposable problems and apply it to the evolution of artificial neural networks. Although this work is preliminary in nature it has a number of advantages over non-coevolutionary approaches. The coevolutionary approach utilizes a divide-and-conquer technique in which species representing simpler subtasks are evolved in separate instances of a genetic algorithm executing in parallel. Collaborations among the species are formed representing complete solutions. Species are created dynamically as needed. Results are presented in which the coevolutionary architecture produces higher quality solutions in fewer evolutionary trials when compared with an alternative non-coevolutionary approach on the problem of evolving cascade networks for parity computation. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #608292. 	[Title] Evolving Complex Structures via Co- operative Coevolution [Abstract] A cooperative coevolutionary approach to learning complex structures is presented which, although preliminary in nature, appears to have a number of advantages over non-coevolutionary approaches. The cooperative coevolutionary approach encourages the parallel evolution of substructures which interact in useful ways to form more complex higher level structures. The architecture is designed to be general enough to permit the inclusion, if appropriate, of a priori knowledge in the form of initial biases towards particular kinds of decompositions. A brief summary of initial results obtained from testing this architecture in several problem domains is presented which shows a significant speedup over more traditional non-coevolutionary approaches. 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 #8874. 	[Title] A Cooperative Coevolutionary Approach to Function Optimization [Abstract] A general model for the coevolution of cooperating species is presented. This model is instantiated and tested in the domain of function optimization, and compared with a traditional GA-based function optimizer. The results are encouraging in two respects. They suggest ways in which the performance of GA and other EA-based optimizers can be improved, and they suggest a new approach to evolving complex structures such as neural networks and rule sets. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #8875. 	[Title] A Coevolutionary Approach to Learning Sequential Decision Rules [Abstract] We present a coevolutionary approach to learning sequential decision rules which appears to have a number of advantages over non-coevolutionary approaches. The coevolutionary approach encourages the formation of stable niches representing simpler sub-behaviors. The evolutionary direction of each subbehavior can be controlled independently, providing an alternative to evolving complex behavior using intermediate training steps. Results are presented showing a significant learning rate speedup over a non-coevolutionary approach in a simulated robot domain. In addition, the results suggest the coevolutionary approach may lead to emer gent problem decompositions. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1133196. 	[Title] Analysis of the Numerical Effects of Parallelism on a Parallel Genetic Algorithm [Abstract] This paper examines the effects of relaxed synchronization on both the numerical and parallel efficiency of parallel genetic algorithms (GAs). We describe a coarse-grain geographically structured parallel genetic algorithm. Our experiments provide preliminary evidence that asynchronous versions of these algorithms have a lower run time than synchronous GAs. Our analysis shows that this improvement is due to (1) decreased synchronization costs and (2) high numerical efficiency (e.g. fewer function evaluations) for the asynchronous GAs. This analysis includes a critique of the utility of traditional parallel performance measures for parallel GAs. 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.

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 #647447. 	[Title] Performance of Multi-Parent Crossover Operators on Numerical Function Optimization Problems [Abstract] The multi-parent scanning crossover, generalizing the traditional uniform crossover, and diagonal crossover, generalizing 1-point (n-point) crossovers, were introduced in [5]. In subsequent publications, see [6, 18, 19], several aspects of multi-parent recombination are discussed. Due to space limitations, however, a full overview of experimental results showing the performance of multi-parent GAs on numerical optimization problems has never been published. This technical report is meant to fill this gap and make results available. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1153816. 	[Title]  [Abstract] Co-evolution of Pursuit and Evasion II: Simulation Methods and Results fl Abstract In a previous SAB paper [10], we presented the scientific rationale for simulating the coevolution of pursuit and evasion strategies. Here, we present an overview of our simulation methods and some results. Our most notable results are as follows. First, co-evolution works to produce good pursuers and good evaders through a pure bootstrapping process, but both types are rather specially adapted to their opponents' current counter-strategies. Second, eyes and brains can also co-evolve within each simulated species for example, pursuers usually evolved eyes on the front of their bodies (like cheetahs), while evaders usually evolved eyes pointing sideways or even backwards (like gazelles). Third, both kinds of coevolution are promoted by allowing spatially distributed populations, gene duplication, and an explicitly spatial morphogenesis program for eyes and brains that allows bilateral symmetry. The paper concludes by discussing some possible applications of simulated pursuit-evasion coevolu tion in biology and entertainment. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #646809. 	[Title] Orgy in the Computer: Multi-Parent Reproduction in Genetic Algorithms [Abstract] In this paper we investigate the phenomenon of multi-parent reproduction, i.e. we study recombination mechanisms where an arbitrary n &gt; 1 number of parents participate in creating children. In particular, we discuss scanning crossover that generalizes the standard uniform crossover and diagonal crossover that generalizes 1-point crossover, and study the effects of different number of parents on the GA behavior. We conduct experiments on tough function optimization problems and observe that by multi-parent operators the performance of GAs can be enhanced significantly. We also give a theoretical foundation by showing how these operators work on distributions. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #175256. 	[Title] Adaptive Global Optimization with Local Search [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #682815. 	[Title] The Role of Development in Genetic Algorithms [Abstract] Technical Report Number CS94-394 Computer Science and Engineering, U.C.S.D. Abstract The developmental mechanisms transforming genotypic to phenotypic forms are typically omitted in formulations of genetic algorithms (GAs) in which these two representational spaces are identical. We argue that a careful analysis of developmental mechanisms is useful when understanding the success of several standard GA techniques, and can clarify the relationships between more recently proposed enhancements. We provide a framework which distinguishes between two developmental mechanisms | learning and maturation | while also showing several common effects on GA search. This framework is used to analyze how maturation and local search can change the dynamics of the GA. We observe that in some contexts, maturation and local search can be incorporated into the fitness evaluation, but illustrate reasons for considering them seperately. Further, we identify contexts in which maturation and local search can be distinguished from the fitness evaluation. Please summarise all the given information.

Paper #1120643 has the title `Genetic Algorithms as Multi-Coordinators in Large-Scale Optimization  
` and the 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.
`.
Question: Which category should Paper #1120643 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #1120643 has the title `Genetic Algorithms as Multi-Coordinators in Large-Scale Optimization  
` and the 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.
`.
Question: Which category should Paper #1120643 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