Above are papers related to the following paper: [Paper_ID] Paper #593544. 	[Title] Toward Simulated Evolution of Machine-Language Iteration [Abstract] We use a simulated evolution search (genetic programming) for the automatic synthesis of small iterative machine-language programs. For an integer register machine with an addition instruction as its sole arithmetic operator, we show that genetic programming can produce exact and general multiplication routines by synthesizing the necessary iterative control structures from primitive machine-language instructions. Our program representation is a virtual register machine that admits arbitrary control flow. Our evolution strategy furthermore does not artificially restrict the synthesis of any control structure; we only place an upper bound on program evaluation time. A program's fitness is the distance between the output produced by a test case and the desired output (multiplication). The test cases exhaustively cover multiplication over a finite subset of the natural numbers (N 10 ); yet the derived solutions constitute general multiplication for the positive integers. For this problem, simulated evolution with a two-point crossover operator examines significantly fewer individuals in finding a solution than random search. Introduction of a small rate of mutation fur ther increases the number of solutions. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #579008. 	[Title] Genetic Programming and Redundancy [Abstract] The Genetic Programming optimization method (GP) elaborated by John Koza [ Koza, 1992 ] is a variant of Genetic Algorithms. The search space of the problem domain consists of computer programs represented as parse trees, and the crossover operator is realized by an exchange of subtrees. Empirical analyses show that large parts of those trees are never used or evaluated which means that these parts of the trees are irrelevant for the solution or redundant. This paper is concerned with the identification of the redundancy occuring in GP. It starts with a mathematical description of the behavior of GP and the conclusions drawn from that description among others explain the "size problem" which denotes the phenomenon that the average size of trees in the population grows with time. 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 #117316. 	[Title] A Simulation of Adaptive Agents in a Hostile Environment [Abstract] In this paper we use the genetic programming technique to evolve programs to control an autonomous agent capable of learning how to survive in a hostile environment. In order to facilitate this goal, agents are run through random environment configurations. Randomly generated programs, which control the interaction of the agent with its environment, are recombined to form better programs. Each generation of the population of agents is placed into the Simulator with the ultimate goal of producing an agent capable of surviving any environment. The environment that an agent is presented consists of other agents, mines, and energy. The goal of this research is to construct a program which when executed will allow an agent (or agents) to correctly sense, and mark, the presence of items (energy and mines) in any environment. The Simulator determines the raw fitness of each agent by interpreting the associated program. General programs are evolved to solve this problem. Different environmental setups are presented to show the generality of the solution. These environments include one agent in a fixed environment, one agent in a fluctuating environment, and multiple agents in a fluctuating environment cooperating together. The genetic programming technique was extremely successful. The average fitness per generation in all three environments tested showed steady improvement. Programs were successfully generated that enabled an agent to handle any possible environment. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #575077. 	[Title] Fitness Landscapes and Difficulty in Genetic Programming [Abstract] The structure of the fitness landscape on which genetic programming operates is examined. The landscapes of a range of problems of known difficulty are analyzed in an attempt to determine which landscape measures correlate with the difficulty of the problem. The autocorrelation of the fitness values of random walks, a measure which has been shown to be related to perceived difficulty using other techniques, is only a weak indicator of the difficulty as perceived by genetic programming. All of these problems show unusually low autocorrelation. Comparison of the range of landscape basin depths at the end of adaptive walks on the landscapes shows good correlation with problem difficulty, over the entire range of problems examined. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #94641. 	[Title] Complexity Compression and Evolution [Abstract] Compression of information is an important concept in the theory of learning. We argue for the hypothesis that there is an inherent compression pressure towards short, elegant and general solutions in a genetic programming system and other variable length evolutionary algorithms. This pressure becomes visible if the size or complexity of solutions are measured without non-effective code segments called introns. The built in parsimony pressure effects complex fitness functions, crossover probability, generality, maximum depth or length of solutions, explicit parsimony, granularity of fitness function, initialization depth or length, and modulariz-ation. Some of these effects are positive and some are negative. In this work we provide a basis for an analysis of these effects and suggestions to overcome the negative implications in order to obtain the balance needed for successful evolution. An empirical investigation that supports our hypothesis is also presented. 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 #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 #210871. 	[Title] Coevolving High-Level Representations [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #594649. 	[Title] Effects of Occam's Razor in Evolving Sigma-Pi Neural Nets [Abstract] Several evolutionary algorithms make use of hierarchical representations of variable size rather than linear strings of fixed length. Variable complexity of the structures provides an additional representational power which may widen the application domain of evolutionary algorithms. The price for this is, however, that the search space is open-ended and solutions may grow to arbitrarily large size. In this paper we study the effects of structural complexity of the solutions on their generalization performance by analyzing the fitness landscape of sigma-pi neural networks. The analysis suggests that smaller networks achieve, on average, better generalization accuracy than larger ones, thus confirming the usefulness of Occam's razor. A simple method for implementing the Occam's razor principle is described and shown to be effective in improv ing the generalization accuracy without limiting their learning capacity. 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.

Above are papers related to the following paper: [Paper_ID] Paper #608190. 	[Title] Evolving Non-Determinism: An Inventive and Efficient Tool for Optimization and Discovery of Strategies [Abstract] In the field of optimization and machine learning techniques, some very efficient and promising tools like Genetic Algorithms (GAs) and Hill-Climbing have been designed. In this same field, the Evolving Non-Determinism (END) model proposes an inventive way to explore the space of states that, combined with the use of simulated co-evolution, remedies some drawbacks of these previous techniques and even allow this model to outperform them on some difficult problems. This new model has been applied to the sorting network problem, a reference problem that challenged many computer scientists, and an original one-player game named Solitaire. For the first problem, the END model has been able to build from scratch some sorting networks as good as the best known for the 16-input problem. It even improved by one comparator a 25 years old result for the 13-input problem! For the Solitaire game, END evolved a strategy comparable to a human designed strategy. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #608191. 	[Title] Incremental Co-evolution of Organisms: A New Approach for Optimization and Discovery of Strategies [Abstract] In the field of optimization and machine learning techniques, some very efficient and promising tools like Genetic Algorithms (GAs) and Hill-Climbing have been designed. In this same field, the Evolving Non-Determinism (END) model presented in this paper proposes an inventive way to explore the space of states that, using the simulated "incremental" co-evolution of some organisms, remedies some drawbacks of these previous techniques and even allow this model to outperform them on some difficult problems. This new model has been applied to the sorting network problem, a reference problem that challenged many computer scientists, and an original one-player game named Solitaire. For the first problem, the END model has been able to build from "scratch" some sorting networks as good as the best known for the 16-input problem. It even improved by one comparator a 25 years old result for the 13-input problem. For the Solitaire game, END evolved a strategy comparable to a human designed strategy. Please summarise all the given information.

Paper #593544 has the title `Toward Simulated Evolution of Machine-Language Iteration  
` and the abstract `We use a simulated evolution search (genetic programming) for the automatic synthesis of small iterative machine-language programs. For an integer register machine with an addition instruction as its sole arithmetic operator, we show that genetic programming can produce exact and general multiplication routines by synthesizing the necessary iterative control structures from primitive machine-language instructions. Our program representation is a virtual register machine that admits arbitrary control flow. Our evolution strategy furthermore does not artificially restrict the synthesis of any control structure; we only place an upper bound on program evaluation time. A program's fitness is the distance between the output produced by a test case and the desired output (multiplication). The test cases exhaustively cover multiplication over a finite subset of the natural numbers (N 10 ); yet the derived solutions constitute general multiplication for the positive integers. For this problem, simulated evolution with a two-point crossover operator examines significantly fewer individuals in finding a solution than random search. Introduction of a small rate of mutation fur ther increases the number of solutions.
`.
Question: Which category should Paper #593544 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #593544 has the title `Toward Simulated Evolution of Machine-Language Iteration  
` and the abstract `We use a simulated evolution search (genetic programming) for the automatic synthesis of small iterative machine-language programs. For an integer register machine with an addition instruction as its sole arithmetic operator, we show that genetic programming can produce exact and general multiplication routines by synthesizing the necessary iterative control structures from primitive machine-language instructions. Our program representation is a virtual register machine that admits arbitrary control flow. Our evolution strategy furthermore does not artificially restrict the synthesis of any control structure; we only place an upper bound on program evaluation time. A program's fitness is the distance between the output produced by a test case and the desired output (multiplication). The test cases exhaustively cover multiplication over a finite subset of the natural numbers (N 10 ); yet the derived solutions constitute general multiplication for the positive integers. For this problem, simulated evolution with a two-point crossover operator examines significantly fewer individuals in finding a solution than random search. Introduction of a small rate of mutation fur ther increases the number of solutions.
`.
Question: Which category should Paper #593544 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