Above are papers related to the following paper: [Paper_ID] Paper #583318. 	[Title] Local Selection [Abstract] Local selection (LS) is a very simple selection scheme in evolutionary algorithms. Individual fitnesses are compared to a fixed threshold, rather than to each other, to decide who gets to reproduce. LS, coupled with fitness functions stemming from the consumption of shared environmental resources, maintains diversity in a way similar to fitness sharing; however it is generally more efficient than fitness sharing, and lends itself to parallel implementations for distributed tasks. While LS is not prone to premature convergence, it applies minimal selection pressure upon the population. LS is therefore more appropriate than other, stronger selection schemes only on certain problem classes. This papers characterizes one broad class of problems in which LS consistently out performs tournament selection. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #152483. 	[Title] An Analysis of the Effects of Neighborhood Size and Shape on Local Selection Algorithms [Abstract] The increasing availability of finely-grained parallel architectures has resulted in a variety of evolutionary algorithms (EAs) in which the population is spatially distributed and local selection algorithms operate in parallel on small, overlapping neighborhoods. The effects of design choices regarding the particular type of local selection algorithm as well as the size and shape of the neighborhood are not particularly well understood and are generally tested empirically. In this paper we extend the techniques used to more formally analyze selection methods for sequential EAs and apply them to local neighborhood models, resulting in a much clearer understanding of the effects of neighborhood size and shape. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #593091. 	[Title] A Survey of Parallel Genetic Algorithms [Abstract] IlliGAL Report No. 97003 May 1997 Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #85352. 	[Title] Evolution in Time and Space The Parallel Genetic Algorithm [Abstract] The parallel genetic algorithm (PGA) uses two major modifications compared to the genetic algorithm. Firstly, selection for mating is distributed. Individuals live in a 2-D world. Selection of a mate is done by each individual independently in its neighborhood. Secondly, each individual may improve its fitness during its lifetime by e.g. local hill-climbing. The PGA is totally asynchronous, running with maximal efficiency on MIMD parallel computers. The search strategy of the PGA is based on a small number of active and intelligent individuals, whereas a GA uses a large population of passive individuals. We will investigate the PGA with deceptive problems and the traveling salesman problem. We outline why and when the PGA is succesful. Abstractly, a PGA is a parallel search with information exchange between the individuals. If we represent the optimization problem as a fitness landscape in a certain configuration space, we see, that a PGA tries to jump from two local minima to a third, still better local minima, by using the crossover operator. This jump is (probabilistically) successful, if the fitness landscape has a certain correlation. We show the correlation for the traveling salesman problem by a configuration space analysis. The PGA explores implicitly the above correlation. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #177998. 	[Title] Complex Environments to Complex Behaviors  FROM COMPLEX ENVIRONMENTS TO COMPLEX BEHAVIORS [Abstract] Adaptation of ecological systems to their environments is commonly viewed through some explicit fitness function defined a priori by the experimenter, or measured a posteriori by estimations based on population size and/or reproductive rates. These methods do not capture the role of environmental complexity in shaping the selective pressures that control the adaptive process. Ecological simulations enabled by computational tools such as the Latent Energy Environments (LEE) model allow us to characterize more closely the effects of environmental complexity on the evolution of adaptive behaviors. LEE is described in this paper. Its motivation arises from the need to vary complexity in controlled and predictable ways, without assuming the relationship of these changes to the adaptive behaviors they engender. This goal is achieved through a careful characterization of environments in which different forms of "energy" are well-defined. A genetic algorithm using endogenous fitness and local selection is used to model the evolutionary process. Individuals in the population are modeled by neural networks with simple sensory-motor systems, and variations in their behaviors are related to interactions with varying environments. We outline the results of three experiments that analyze different sources of environmental complexity and their effects on the collective behaviors of evolving populations. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1110515. 	[Title] Environmental Effects on Minimal Behaviors in the Minimat World [Abstract] The structure of an environment affects the behaviors of the organisms that have evolved in it. How is that structure to be described, and how can its behavioral consequences be explained and predicted? We aim to establish initial answers to these questions by simulating the evolution of very simple organisms in simple environments with different structures. Our artificial creatures, called "minimats," have neither sensors nor memory and behave solely by picking amongst the actions of moving, eating, reproducing, and sitting, according to an inherited probability distribution. Our simulated environments contain only food (and multiple minimats) and are structured in terms of their spatial and temporal food density and the patchiness with which the food appears. Changes in these environmental parameters affect the evolved behaviors of minimats in different ways, and all three parameters are of importance in describing the minimat world. One of the most useful behavioral strategies that evolves is "looping" movement, which allows minimats-despite their lack of internal state-to match their behavior to the temporal (and spatial) structure of their environment. Ultimately we find that minimats construct their own environments through their individual behaviors, making the study of the impact of global environment structure on individual behavior much more complex. 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 #583318 has the title `Local Selection  
` and the abstract `Local selection (LS) is a very simple selection scheme in evolutionary algorithms. Individual fitnesses are compared to a fixed threshold, rather than to each other, to decide who gets to reproduce. LS, coupled with fitness functions stemming from the consumption of shared environmental resources, maintains diversity in a way similar to fitness sharing; however it is generally more efficient than fitness sharing, and lends itself to parallel implementations for distributed tasks. While LS is not prone to premature convergence, it applies minimal selection pressure upon the population. LS is therefore more appropriate than other, stronger selection schemes only on certain problem classes. This papers characterizes one broad class of problems in which LS consistently out performs tournament selection.
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Question: Which category should Paper #583318 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #583318 has the title `Local Selection  
` and the abstract `Local selection (LS) is a very simple selection scheme in evolutionary algorithms. Individual fitnesses are compared to a fixed threshold, rather than to each other, to decide who gets to reproduce. LS, coupled with fitness functions stemming from the consumption of shared environmental resources, maintains diversity in a way similar to fitness sharing; however it is generally more efficient than fitness sharing, and lends itself to parallel implementations for distributed tasks. While LS is not prone to premature convergence, it applies minimal selection pressure upon the population. LS is therefore more appropriate than other, stronger selection schemes only on certain problem classes. This papers characterizes one broad class of problems in which LS consistently out performs tournament selection.
`.
Question: Which category should Paper #583318 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