Above are papers related to the following paper: [Paper_ID] Paper #459214. 	[Title] The Impact of External Dependency in Genetic Programming Primitives [Abstract] Both control and data dependencies among primitives impact the behavioural consistency of subprograms in genetic programming solutions. Behavioural consistency in turn impacts the ability of genetic programming to identify and promote appropriate subprograms. We present the results of modelling dependency through a parameterized problem in which a subprogram exhibits internal and external dependency levels that change as the subprogram is successively combined into larger subsolutions. We find that the key difference between non-existent and "full" external dependency is a longer time to solution identification and a lower likelihood of success as shown by increased difficulty in identifying and promoting correct subprograms. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #66563. 	[Title] An Experimental Analysis of Schema Creation, Propagation and Disruption in Genetic Programming [Abstract] In this paper we first review the main results in the theory of schemata in Genetic Programming (GP) and summarise a new GP schema theory which is based on a new definition of schema. Then we study the creation, propagation and disruption of this new form of schemata in real runs, for standard crossover, one-point crossover and selection only. Finally, we discuss these results in the light our GP schema theorem. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #141324. 	[Title] An Analysis of the MAX Problem in Genetic Programming hold only in some cases, in [Abstract] We present a detailed analysis of the evolution of genetic programming (GP) populations using the problem of finding a program which returns the maximum possible value for a given terminal and function set and a depth limit on the program tree (known as the MAX problem). We confirm the basic message of [ Gathercole and Ross, 1996 ] that crossover together with program size restrictions can be responsible for premature convergence to a suboptimal solution. We show that this can happen even when the population retains a high level of variety and show that in many cases evolution from the sub-optimal solution to the solution is possible if sufficient time is allowed. In both cases theoretical models are presented and compared with actual runs. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1113742. 	[Title] Testing the Robustness of the Genetic Algorithm on the Floating Building Block Representation. [Abstract] Recent studies on a floating building block representation for the genetic algorithm (GA) suggest that there are many advantages to using the floating representation. This paper investigates the behavior of the GA on floating representation problems in response to three different types of pressures: (1) a reduction in the amount of genetic material available to the GA during the problem solving process, (2) functions which have negative-valued building blocks, and (3) randomizing non-coding segments. Results indicate that the GA's performance on floating representation problems is very robust. Significant reductions in genetic material (genome length) may be made with relatively small decrease in performance. The GA can effectively solve problems with negative building blocks. Randomizing non-coding segments appears to improve rather than harm GA performance. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1128982. 	[Title] Learning Recursive Sequences via Evolution of Machine-Language Programs [Abstract] We use directed search techniques in the space of computer programs to learn recursive sequences of positive integers. Specifically, the integer sequences of squares, x 2 ; cubes, x 3 ; factorial, x!; and Fibonacci numbers are studied. Given a small finite prefix of a sequence, we show that three directed searches|machine-language genetic programming with crossover, exhaustive iterative hill climbing, and a hybrid (crossover and hill climbing)|can automatically discover programs that exactly reproduce the finite target prefix and, moreover, that correctly produce the remaining sequence up to the underlying machine's precision. Our machine-language representation is generic|it contains instructions for arithmetic, register manipulation and comparison, and control flow. We also introduce an output instruction that allows variable-length sequences as result values. Importantly, this representation does not contain recursive operators; recursion, when needed, is automatically synthesized from primitive instructions. For a fixed set of search parameters (e.g., instruction set, program size, fitness criteria), we compare the efficiencies of the three directed search techniques on the four sequence problems. For this parameter set, an evolutionary-based search always outperforms exhaustive hill climbing as well as undirected random search. Since only the prefix of the target sequence is variable in our experiments, we posit that this approach to sequence induction is potentially quite general. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #593942. 	[Title] Why Ants are Hard genetic programming, simulated annealing and hill climbing performance is shown not [Abstract] The problem of programming an artificial ant to follow the Santa Fe trail is used as an example program search space. Analysis of shorter solutions shows they have many of the characteristics often ascribed to manually coded programs. Enumeration of a small fraction of the total search space and random sampling characterise it as rugged with many multiple plateaus split by deep valleys and many local and global optima. This suggests it is difficult for hill climbing algorithms. Analysis of the program search space in terms of fixed length schema suggests it is highly deceptive and that for the simplest solutions large building blocks must be assembled before they have above average fitness. In some cases we show solutions cannot be assembled using a fixed representation from small building blocks of above average fitness. These suggest the Ant problem is difficult for Genetic Algorithms. Random sampling of the program search space suggests on average the density of global optima changes only slowly with program size but the density of neutral networks linking points of the same fitness grows approximately linearly with program length. This is part of the cause of bloat. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #243483. 	[Title] A Comparison of Crossover and Mutation in Genetic Programming [Abstract] This paper presents a large and systematic body of data on the relative effectiveness of mutation, crossover, and combinations of mutation and crossover in genetic programming (GP). The literature of traditional genetic algorithms contains related studies, but mutation and crossover in GP differ from their traditional counterparts in significant ways. In this paper we present the results from a very large experimental data set, the equivalent of approximately 12,000 typical runs of a GP system, systematically exploring a range of parameter settings. The resulting data may be useful not only for practitioners seeking to optimize parameters for GP runs, but also for theorists exploring issues such as the role of building blocks in GP. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #8865. 	[Title] Using Genetic Algorithms to Explore Pattern Recognition in the Immune System  COMMENTS WELCOME [Abstract] This paper describes an immune system model based on binary strings. The purpose of the model is to study the pattern recognition processes and learning that take place at both the individual and species levels in the immune system. The genetic algorithm (GA) is a central component of the model. The paper reports simulation experiments on two pattern recognition problems that are relevant to natural immune systems. Finally, it reviews the relation between the model and explicit fitness sharing techniques for genetic algorithms, showing that the immune system model implements a form of implicit fitness sharing. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #35. 	[Title] 4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley. [Abstract] To implement a specific application, you should only have to change the file app.c. Section 2 describes the routines in app.c in detail. If you use additional variables for your specific problem, the easiest method of making them available to other program units is to declare them in sga.h and external.h. However, take care that you do not redeclare existing variables. Two example applications files are included in the SGA-C distribution. The file app1.c performs the simple example problem included with the Pascal version; finding the maximum of x 10 , where x is an integer interpretation of a chromosome. A slightly more complex application is include in app2.c. This application illustrates two features that have been added to SGA-C. The first of these is the ithruj2int function, which converts bits i through j in a chromosome to an integer. The second new feature is the utility pointer that is associated with each population member. The example application interprets each chromosome as a set of concatenated integers in binary form. The lengths of these integer fields is determined by the user-specified value of field size, which is read in by the function app data(). The field size must be less than the smallest of the chromosome length and the length of an unsigned integer. An integer array for storing the interpreted form of each chromosome is dynamically allocated and assigned to the chromosome's utility pointer in app malloc(). The ithruj2int routine (see utility.c) is used to translate each chromosome into its associated vector. The fitness for each chromosome is simply the sum of the squares of these integers. This example application will function for any chromosome length. SGA-C is intended to be a simple program for first-time GA experimentation. It is not intended to be definitive in terms of its efficiency or the grace of its implementation. The authors are interested in the comments, criticisms, and bug reports from SGA-C users, so that the code can be refined for easier use in subsequent versions. Please email your comments to rob@galab2.mh.ua.edu, or write to TCGA: The authors gratefully acknowledge support provided by NASA under Grant NGT-50224 and support provided by the National Science Foundation under Grant CTS-8451610. We also thank Hillol Kargupta for donating his tournament selection implementation. Booker, L. B. (1982). Intelligent behavior as an adaptation to the task environment (Doctoral dissertation, Technical Report No. 243. Ann Arbor: University of Michigan, Logic of Computers Group). Dissertations Abstracts International, 43(2), 469B. (University Microfilms No. 8214966) Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #62634. 	[Title] A Study in Program Response and the Negative Effects of Introns in Genetic Programming [Abstract] The standard method of obtaining a response in tree-based genetic programming is to take the value returned by the root node. In non-tree representations, alternate methods have been explored. One alternative is to treat a specific location in indexed memory as the response value when the program terminates. The purpose of this paper is to explore the applicability of this technique to tree-structured programs and to explore the intron effects that these studies bring to light. This paper's experimental results support the finding that this memory-based program response technique is an improvement for some, but not all, problems. In addition, this paper's experimental results support the finding that, contrary to past research and speculation, the addition or even facilitation of introns can seriously degrade the search performance of genetic programming. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #575402. 	[Title] When Will a Genetic Algorithm Outperform Hill Climbing? [Abstract] We analyze a simple hill-climbing algorithm (RMHC) that was previously shown to outperform a genetic algorithm (GA) on a simple "Royal Road" function. We then analyze an "idealized" genetic algorithm (IGA) that is significantly faster than RMHC and that gives a lower bound for GA speed. We identify the features of the IGA that give rise to this speedup, and discuss how these features can be incorporated into a real GA. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1129778. 	[Title] Modeling Building-Block Interdependency  Dynamical and Evolutionary Machine Organization Group [Abstract] The Building-Block Hypothesis appeals to the notion of problem decomposition and the assembly of solutions from sub-solutions. Accordingly, there have been many varieties of GA test problems with a structure based on building-blocks. Many of these problems use deceptive fitness functions to model interdependency between the bits within a block. However, very few have any model of interdependency between building-blocks; those that do are not consistent in the type of interaction used intra-block and inter-block. This paper discusses the inadequacies of the various test problems in the literature and clarifies the concept of building-block interdependency. We formulate a principled model of hierarchical interdependency that can be applied through many levels in a consistent manner and introduce Hierarchical If-and-only-if (H-IFF) as a canonical example. We present some empirical results of GAs on H-IFF showing that if population diversity is maintained and linkage is tight then the GA is able to identify and manipulate building-blocks over many levels of assembly, as the Building-Block Hypothesis suggests. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #28851. 	[Title] Program Search with a Hierarchical Variable Length Representation: Genetic Programming, Simulated Annealing and Hill Climbing [Abstract] This paper presents a comparison of Genetic Programming(GP) with Simulated Annealing (SA) and Stochastic Iterated Hill Climbing (SIHC) based on a suite of program discovery problems which have been previously tackled only with GP. All three search algorithms employ the hierarchical variable length representation for programs brought into recent prominence with the GP paradigm [8]. We feel it is not intuitively obvious that mutation-based adaptive search can handle program discovery yet, to date, for each GP problem we have tried, SA or SIHC also work. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1153853. 	[Title] Voting for Schemata [Abstract] The schema theorem states that implicit parallel search is behind the power of the genetic algorithm. We contend that chromosomes can vote, proportionate to their fitness, for candidate schemata. We maintain a population of binary strings and ternary schemata. The string population not only works on solving its problem domain, but it supplies fitness for the schema population, which indirectly can solve the original problem. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #59715. 	[Title] Genetic Programming Exploratory Power and the Discovery of Functions [Abstract] Hierarchical genetic programming (HGP) approaches rely on the discovery, modification, and use of new functions to accelerate evolution. This paper provides a qualitative explanation of the improved behavior of HGP, based on an analysis of the evolution process from the dual perspective of diversity and causality. From a static point of view, the use of an HGP approach enables the manipulation of a population of higher diversity programs. Higher diversity increases the exploratory ability of the genetic search process, as demonstrated by theoretical and experimental fitness distributions and expanded structural complexity of individuals. From a dynamic point of view, an analysis of the causality of the crossover operator suggests that HGP discovers and exploits useful structures in a bottom-up, hierarchical manner. Diversity and causality are complementary, affecting exploration and exploitation in genetic search. Unlike other machine learning techniques that need extra machinery to control the tradeoff between them, HGP automatically trades off exploration and exploitation. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #593859. 	[Title] Price's Theorem and the MAX Problem [Abstract] We present a detailed analysis of the evolution of GP populations using the problem of finding a program which returns the maximum possible value for a given terminal and function set and a depth limit on the program tree (known as the MAX problem). We confirm the basic message of [ Gathercole and Ross, 1996 ] that crossover together with program size restrictions can be responsible for premature convergence to a sub-optimal solution. We show that this can happen even when the population retains a high level of variety and show that in many cases evolution from the sub-optimal solution to the solution is possible if sufficient time is allowed. In both cases theoretical models are presented and compared with actual runs. Experimental evidence is presented that Price's Covariance and Selection Theorem can be applied to GP populations and the practical effect of program size restrictions are noted. Finally we show that covariance between gene frequency and fitness in the first few generations can be used to predict the course of GP runs. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #459206. 	[Title] Where Does the Good Stuff Go, and Why? How contextual semantics influences program structure in [Abstract] Using deliberately designed primitive sets, we investigate the relationship between context-based expression mechanisms and the size, height and density of genetic program trees during the evolutionary process. We show that contextual semantics influence the composition, location and flows of operative code in a program. In detail we analyze these dynamics and discuss the impact of our findings on micro-level descriptions of genetic programming. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1123530. 	[Title] How Fitness Structure Affects Subsolution Acquisition in Genetic Programming [Abstract] We define fitness structure in genetic programming to be the mapping between the subprograms of a program and their respective fitness values. This paper shows how various fitness structures of a problem with independent subsolutions relate to the acquisition of sub-solutions. The rate of subsolution acquisition is found to be directly correlated with fitness structure whether that structure is uniform, linear or exponential. An understanding of fitness structure provides partial insight into the complicated relationship between fitness function and the outcome of genetic programming's search. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #459213. 	[Title] Using a Distance Metric on Genetic Programs to Understand Genetic Operators [Abstract] I describe a distance metric called "edit" distance which quantifies the syntactic difference between two genetic programs. In the context of one specific problem, the 6 bit multiplexor, I use the metric to analyze the amount of new material introduced by different crossover operators, the difference among the best individuals of a population and the difference among the best individuals and the rest of the population. The relationships between these data and run performance are imprecise but they are sufficiently interesting to encourage encourage further investigation into the use of edit distance. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #459216. 	[Title] Neural Programming and an Internal Reinforcement Policy [Abstract] An important reason for the continued popularity of Artificial Neural Networks (ANNs) in the machine learning community is that the gradient-descent backpropagation procedure gives ANNs a locally optimal change procedure and, in addition, a framework for understanding the ANN learning performance. Genetic programming (GP) is also a successful evolutionary learning technique that provides powerful parameterized primitive constructs. Unlike ANNs, though, GP does not have such a principled procedure for changing parts of the learned system based on its current performance. This paper introduces Neural Programming, a connectionist representation for evolving programs that maintains the benefits of GP. The connectionist model of Neural Programming allows for a regression credit-blame procedure in an evolutionary learning system. We describe a general method for an informed feedback mechanism for Neural Programming, Internal Reinforcement. We introduce an Internal Reinforcement procedure and demon strate its use through an illustrative experiment. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #22229. 	[Title] The Automatic Programming of Agents that Learn Mental Models and Create Simple Plans of Action [Abstract] An essential component of an intelligent agent is the ability to notice, encode, store, and utilize information about its environment. Traditional approaches to program induction have focused on evolving functional or reactive programs. This paper presents MAPMAKER, an approach to the automatic generation of agents that discover information about their environment, encode this information for later use, and create simple plans utilizing the stored mental models. In this approach, agents are multipart computer programs that communicate through a shared memory. Both the programs and the representation scheme are evolved using genetic programming. An illustrative problem of 'gold' collection is used to demonstrate the approach in which one part of a program makes a map of the world and stores it in memory, and the other part uses this map to find the gold The results indicate that the approach can evolve programs that store simple representations of their environments and use these representations to produce simple plans. 1. Introduction Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #246618. 	[Title] A comparison of the fixed and floating building block representation in the genetic algorithm [Abstract] This article compares the traditional, fixed problem representation style of a genetic algorithm (GA) with a new floating representation in which the building blocks of a problem are not fixed at specific locations on the individuals of the population. In addition, the effects of non-coding segments on both of these representations is studied. Non-coding segments are a computational model of non-coding DNA and floating building blocks mimic the location independence of genes. The fact that these structures are prevalent in natural genetic systems suggests that they may provide some advantages to the evolutionary process. Our results show that there is a significant difference in how GAs solve a problem in the fixed and floating representations. GAs are able to maintain a more diverse population with the floating representation. The combination of non-coding segments and floating building blocks appears to encourage a GA to take advantage of its parallel search and recombination abilities. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #103515. 	[Title] The Royal Road for Genetic Algorithms: Fitness Landscapes and GA Performance [Abstract] Genetic algorithms (GAs) play a major role in many artificial-life systems, but there is often little detailed understanding of why the GA performs as it does, and little theoretical basis on which to characterize the types of fitness landscapes that lead to successful GA performance. In this paper we propose a strategy for addressing these issues. Our strategy consists of defining a set of features of fitness landscapes that are particularly relevant to the GA, and experimentally studying how various configurations of these features affect the GA's performance along a number of dimensions. In this paper we informally describe an initial set of proposed feature classes, describe in detail one such class ("Royal Road" functions), and present some initial experimental results concerning the role of crossover and "building blocks" on landscapes constructed from features of this class. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #578780. 	[Title] Towards Automatic Discovery of Building Blocks in Genetic Programming [Abstract] This paper presents an algorithm for the discovery of building blocks in genetic programming (GP) called adaptive representation through learning (ARL). The central idea of ARL is the adaptation of the problem representation, by extending the set of terminals and functions with a set of evolvable subroutines. The set of subroutines extracts common knowledge emerging during the evolutionary process and acquires the necessary structure for solving the problem. ARL supports subroutine creation and deletion. Subroutine creation or discovery is performed automatically based on the differential parent-offspring fitness and block activation. Subroutine deletion relies on a utility measure similar to schema fitness over a window of past generations. The technique described is tested on the problem of controlling an agent in a dynamic and non-deterministic environment. The automatic discovery of subroutines can help scale up the GP technique to complex problems. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1153897. 	[Title]  [Abstract] An investigation into the dynamics of Genetic Programming applied to chaotic time series prediction is reported. An interesting characteristic of adaptive search techniques is their ability to perform well in many problem domains while failing in others. Because of Genetic Programming's flexible tree structure, any particular problem can be represented in myriad forms. These representations have variegated effects on search performance. Therefore, an aspect of fundamental engineering significance is to find a representation which, when acted upon by Genetic Programming operators, optimizes search performance. We discover, in the case of chaotic time series prediction, that the representation commonly used in this domain does not yield optimal solutions. Instead, we find that the population converges onto one "accurately replicating" tree before other trees can be explored. To correct for this premature convergence we make a simple modification to the crossover operator. In this paper we review previous work with GP time series prediction, pointing out an anomalous result related to overlearning, and report the improvement effected by our modified crossover operator. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1127913. 	[Title] THE OPTIONS DESIGN EXPLORATION SYSTEM Reference Manual and User Guide Version B2.1 [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #54131. 	[Title] The Troubling Aspects of a Building Block Hypothesis for Genetic Programming [Abstract] In this paper we carefully formulate a Schema Theorem for Genetic Programming (GP) using a schema definition that accounts for the variable length and the non-homologous nature of GP's representation. In a manner similar to early GA research, we use interpretations of our GP Schema Theorem to obtain a GP Building Block definition and to state a "classical" Building Block Hypothesis (BBH): that GP searches by hierarchically combining building blocks. We report that this approach is not convincing for several reasons: it is difficult to find support for the promotion and combination of building blocks solely by rigourous interpretation of a GP Schema Theorem; even if there were such support for a BBH, it is empirically questionable whether building blocks always exist because partial solutions of consistently above average fitness and resilience to disruption are not assured; also, a BBH constitutes a narrow and imprecise account of GP search behavior. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1128314. 	[Title] Distributed Collective Adaptation Applied to a Hard Combinatorial Optimization Problem [Abstract] We utilize collective memory to integrate weak and strong search heuristics to find cliques in FC, a family of graphs. We construct FC such that pruning of partial solutions will be ineffective. Each weak heuristic maintains a local cache of the collective memory. We examine the impact on the distributed search from the various characteristics of the distribution of the collective memory, the search algorithms, and our family of graphs. We find the distributed search performs better than the individuals, even though the space of partial solutions is combinatorially explosive. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #66556. 	[Title] The Schema Theorem and Price's Theorem [Abstract] Holland's Schema Theorem is widely taken to be the foundation for explanations of the power of genetic algorithms (GAs). Yet some dissent has been expressed as to its implications. Here, dissenting arguments are reviewed and elaborated upon, explaining why the Schema Theorem has no implications for how well a GA is performing. Interpretations of the Schema Theorem have implicitly assumed that a correlation exists between parent and offspring fitnesses, and this assumption is made explicit in results based on Price's Covariance and Selection Theorem. Schemata do not play a part in the performance theorems derived for representations and operators in general. However, schemata re-emerge when recombination operators are used. Using Geiringer's recombination distribution representation of recombination operators, a "missing" schema theorem is derived which makes explicit the intuition for when a GA should perform well. Finally, the method of "adaptive landscape" analysis is examined and counterexamples offered to the commonly used correlation statistic. Instead, an alternative statistic | the transmission function in the fitness domain | is proposed as the optimal statistic for estimating GA performance from limited samples. Please summarise all the given information.

Paper #459214 has the title `The Impact of External Dependency in Genetic Programming Primitives  
` and the abstract `Both control and data dependencies among primitives impact the behavioural consistency of subprograms in genetic programming solutions. Behavioural consistency in turn impacts the ability of genetic programming to identify and promote appropriate subprograms. We present the results of modelling dependency through a parameterized problem in which a subprogram exhibits internal and external dependency levels that change as the subprogram is successively combined into larger subsolutions. We find that the key difference between non-existent and "full" external dependency is a longer time to solution identification and a lower likelihood of success as shown by increased difficulty in identifying and promoting correct subprograms. 
`.
Question: Which category should Paper #459214 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #459214 has the title `The Impact of External Dependency in Genetic Programming Primitives  
` and the abstract `Both control and data dependencies among primitives impact the behavioural consistency of subprograms in genetic programming solutions. Behavioural consistency in turn impacts the ability of genetic programming to identify and promote appropriate subprograms. We present the results of modelling dependency through a parameterized problem in which a subprogram exhibits internal and external dependency levels that change as the subprogram is successively combined into larger subsolutions. We find that the key difference between non-existent and "full" external dependency is a longer time to solution identification and a lower likelihood of success as shown by increased difficulty in identifying and promoting correct subprograms. 
`.
Question: Which category should Paper #459214 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