Above are papers related to the following paper: [Paper_ID] Paper #447224. 	[Title] A COMPRESSION ALGORITHM FOR PROBABILITY TRANSITION MATRICES [Abstract] This paper describes a compression algorithm for probability transition matrices. The compressed matrix is itself a probability transition matrix. In general the compression is not error-free, but the error appears to be small even for high levels of compression. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1112075. 	[Title] In Proceedings of the 1997 Sian Kaan International Workshop on Neural Networks and Neurocontrol. Real-Valued [Abstract] 2 Neural Network & Machine Learning Laboratory Computer Science Department Brigham Young University Provo, UT 84602, USA Email: martinez@cs.byu.edu WWW: http://axon.cs.byu.edu Abstract. Many neural network models must be trained by finding a set of real-valued weights that yield high accuracy on a training set. Other learning models require weights on input attributes that yield high leave-one-out classification accuracy in order to avoid problems associated with irrelevant attributes and high dimensionality. In addition, there are a variety of general problems for which a set of real values must be found which maximize some evaluation function. This paper presents an algorithm for doing a schemata search over a real-valued weight space to find a set of weights (or other real values) that yield high values for a given evaluation function. The algorithm, called the Real-Valued Schemata Search (RVSS), uses the BRACE statistical technique [Moore & Lee, 1993] to determine when to narrow the search space. This paper details the RVSS approach and gives initial empirical results. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #134219. 	[Title] An Overview of Evolutionary Computation [Abstract] Evolutionary computation uses computational models of evolution - ary processes as key elements in the design and implementation of computer-based problem solving systems. In this paper we provide an overview of evolutionary computation, and describe several evolutionary algorithms that are currently of interest. Important similarities and di fferences are noted, which lead to a discussion of important issues that need to be resolved, and items for future research. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #81714. 	[Title] EVOLVING REPRESENTATIONS OF DESIGN CASES AND THEIR USE IN CREATIVE DESIGN [Abstract] In case-based design, the adaptation of a design case to new design requirements plays an important role. If it is sufficient to adapt a predefined set of design parameters, the task is easily automated. If, however, more far-reaching, creative changes are required, current systems provide only limited success. This paper describes an approach to creative design adaptation based on the notion of creativity as 'goal oriented shift of focus of a search process'. An evolving representation is used to restructure the search space so that designs similar to the example case lie in the focus of the search. This focus is than used as a starting point to create new designs. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #503893. 	[Title] An Evolutionary Approach to Combinatorial Optimization Problems [Abstract] The paper reports on the application of genetic algorithms, probabilistic search algorithms based on the model of organic evolution, to NP-complete combinatorial optimization problems. In particular, the subset sum, maximum cut, and minimum tardy task problems are considered. Except for the fitness function, no problem-specific changes of the genetic algorithm are required in order to achieve results of high quality even for the problem instances of size 100 used in the paper. For constrained problems, such as the subset sum and the minimum tardy task, the constraints are taken into account by incorporating a graded penalty term into the fitness function. Even for large instances of these highly multimodal optimization problems, an iterated application of the genetic algorithm is observed to find the global optimum within a number of runs. As the genetic algorithm samples only a tiny fraction of the search space, these results are quite encouraging. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #164885. 	[Title] Analyzing GAs Using Markov Models with Semantically Ordered and Lumped States [Abstract] At the previous FOGA workshop, we presented some initial results on using Markov models to analyze the transient behavior of genetic algorithms (GAs) being used as function optimizers (GAFOs). In that paper, the states of the Markov model were ordered via a simple and mathematically convenient lexicographic ordering used initially by Nix and Vose. In this paper, we explore alternative orderings of states based on interesting semantic properties such as average fitness, degree of homogeneity, average attractive force, etc. We also explore lumping techniques for reducing the size of the state space. Analysis of these reordered and lumped Markov models provides new insights into the transient behavior of GAs in general and GAFOs in particular. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #134199. 	[Title] Using Markov Chains to Analyze GAFOs [Abstract] Our theoretical understanding of the properties of genetic algorithms (GAs) being used for function optimization (GAFOs) is not as strong as we would like. Traditional schema analysis provides some first order insights, but doesn't capture the non-linear dynamics of the GA search process very well. Markov chain theory has been used primarily for steady state analysis of GAs. In this paper we explore the use of transient Markov chain analysis to model and understand the behavior of finite population GAFOs observed while in transition to steady states. This approach appears to provide new insights into the circumstances under which GAFOs will (will not) perform well. Some preliminary results are presented and an initial evaluation of the merits of this approach is provided. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1114331. 	[Title] Island Model Genetic Algorithms and Linearly Separable Problems [Abstract] Parallel Genetic Algorithms have often been reported to yield better performance than Genetic Algorithms which use a single large panmictic population. In the case of the Island Model Genetic Algorithm, it has been informally argued that having multiple subpopulations helps to preserve genetic diversity, since each island can potentially follow a different search trajectory through the search space. On the other hand, linearly separable functions have often been used to test Island Model Genetic Algorithms; it is possible that Island Models are particular well suited to separable problems. We look at how Island Models can track multiple search trajectories using the infinite population models of the simple genetic algorithm. We also introduce a simple model for better understanding when Island Model Genetic Algorithms may have an advantage when processing linearly separable problems. Please summarise all the given information.

Paper #447224 has the title `A COMPRESSION ALGORITHM FOR PROBABILITY TRANSITION MATRICES  
` and the abstract `This paper describes a compression algorithm for probability transition matrices. The compressed matrix is itself a probability transition matrix. In general the compression is not error-free, but the error appears to be small even for high levels of compression. 
`.
Question: Which category should Paper #447224 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #447224 has the title `A COMPRESSION ALGORITHM FOR PROBABILITY TRANSITION MATRICES  
` and the abstract `This paper describes a compression algorithm for probability transition matrices. The compressed matrix is itself a probability transition matrix. In general the compression is not error-free, but the error appears to be small even for high levels of compression. 
`.
Question: Which category should Paper #447224 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