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 #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 #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 #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 #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 #164885 has the title `Analyzing GAs Using Markov Models with Semantically Ordered and Lumped States  
` and the 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.
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Question: Which category should Paper #164885 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #164885 has the title `Analyzing GAs Using Markov Models with Semantically Ordered and Lumped States  
` and the 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.
`.
Question: Which category should Paper #164885 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