Above are papers related to the following paper: [Paper_ID] Paper #54132. 	[Title] Causality in Genetic Programming [Abstract] Causality relates changes in the structure of an object with the effects of such changes, that is changes in the properties or behavior of the object. This paper analyzes the concept of causality in Genetic Programming (GP) and suggests how it can be used in adapting control parameters for speeding up GP search. We first analyze the effects of crossover to show the weak causality of the GP representation and operators. Hierarchical GP approaches based on the discovery and evolution of functions amplify this phenomenon. However, selection gradually retains strongly causal changes. Causality is correlated to search space exploitation and is discussed in the context of the exploration-exploitation tradeoff. The results described argue for a bottom-up GP evolutionary thesis. Finally, new developments based on the idea of GP architecture evolution (Koza, 1994a) are discussed from the causality perspective. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #593921. 	[Title] Position Paper, Workshop on Evolutionary Computation with Variable Size Representation, ICGA, Fitness Causes Bloat in [Abstract] We argue based upon the numbers of representations of given length, that increase in representation length is inherent in using a fixed evaluation function with a discrete but variable length representation. Two examples of this are analysed, including the use of Price's Theorem. Both examples confirm the tendency for solutions to grow in size is caused by fitness based selection. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1128978. 	[Title] The MAX Problem for Genetic Programming Highlighting an Adverse Interaction between the Crossover Operator and [Abstract] The Crossover operator is common to most implementations of Genetic Programming (GP). Another, usually unavoidable, factor is some form of restriction on the size of trees in the GP population. This paper concentrates on the interaction between the Crossover operator and a restriction on tree depth demonstrated by the MAX problem, which involves returning the largest possible value for given function and terminal sets. Some characteristics and inadequacies of Crossover in `normal' use are highlighted and discussed. Subtree discovery and movement takes place mostly near the leaf nodes, with nodes near the root left untouched. Diversity drops quickly to zero near the root node in the tree population. GP is then unable to create `fitter' trees via the crossover operator, leaving a Mutation operator as the only common, but ineffective, route to discovery of `fitter' trees. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1128985. 	[Title] Evolving Visual Routines  Architecture and Planning, [Abstract]  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 #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 #1117476. 	[Title] Hierarchical Self-Organization in Genetic Programming [Abstract] This paper presents an approach to automatic discovery of functions in Genetic Programming. The approach is based on discovery of useful building blocks by analyzing the evolution trace, generalizing blocks to define new functions, and finally adapting the problem representation on-the-fly. Adaptating the representation determines a hierarchical organization of the extended function set which enables a restructuring of the search space so that solutions can be found more easily. Measures of complexity of solution trees are defined for an adaptive representation framework. The minimum description length principle is applied to justify the feasibility of approaches based on a hierarchy of discovered functions and to suggest alternative ways of defining a problem's fitness function. Preliminary empirical results are presented. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #44455. 	[Title] Evolving Visually Guided Robots [Abstract] A version of this paper appears in: Proceedings of SAB92, the Second International Conference on Simulation of Adaptive Behaviour J.-A. Meyer, H. Roitblat, and S. Wilson, editors, MIT Press Bradford Books, Cambridge, MA, 1993. 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 #94639. 	[Title] Genetic Programming and Data Structures [Abstract]  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 #210871. 	[Title] Coevolving High-Level Representations [Abstract]  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 #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 #96845. 	[Title] Evolution-based Discovery of Hierarchical Behaviors [Abstract] Procedural representations of control policies have two advantages when facing the scale-up problem in learning tasks. First they are implicit, with potential for inductive generalization over a very large set of situations. Second they facilitate modularization. In this paper we compare several randomized algorithms for learning modular procedural representations. The main algorithm, called Adaptive Representation through Learning (ARL) is a genetic programming extension that relies on the discovery of subroutines. ARL is suitable for learning hierarchies of subroutines and for constructing policies to complex tasks. ARL was successfully tested on a typical reinforcement learning problem of controlling an agent in a dynamic and nondeterministic environment where the discovered subroutines correspond to agent behaviors. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #593104. 	[Title] Genetic Programming Bloat with Dynamic Fitness [Abstract] Technical Report: CSRP-97-29, 3 December 1997 Abstract In artificial evolution individuals which perform as their parents are usually rewarded identically to their parents. We note that Nature is more dynamic and there may be a penalty to pay for doing the same thing as your parents. We report two sets of experiments where static fitness functions are firstly augmented by a penalty for unchanged offspring and secondly the static fitness case is replaced by randomly generated dynamic test cases. We conclude genetic programming, when evolving artificial ant control programs, is surprisingly little effected by large penalties and program growth is observed in all our experiments. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #594387. 	[Title] Evolving Compact Solutions in Genetic Programming: A Case Study [Abstract] Genetic programming (GP) is a variant of genetic algorithms where the data structures handled are trees. This makes GP especially useful for evolving functional relationships or computer programs, as both can be represented as trees. Symbolic regression is the determination of a function dependence y = g(x) that approximates a set of data points (x i ; y i ). In this paper the feasibility of symbolic regression with GP is demonstrated on two examples taken from different domains. Furthermore several suggested methods from literature are compared that are intended to improve GP performance and the readability of solutions by taking into account introns or redundancy that occurs in the trees and keeping the size of the trees small. The experiments show that GP is an elegant and useful tool to derive complex functional dependencies on numerical data. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #96851. 	[Title] Balancing Accuracy and Parsimony in Genetic Programming 1 [Abstract] Genetic programming is distinguished from other evolutionary algorithms in that it uses tree representations of variable size instead of linear strings of fixed length. The flexible representation scheme is very important because it allows the underlying structure of the data to be discovered automatically. One primary difficulty, however, is that the solutions may grow too big without any improvement of their generalization ability. In this paper we investigate the fundamental relationship between the performance and complexity of the evolved structures. The essence of the parsimony problem is demonstrated empirically by analyzing error landscapes of programs evolved for neural network synthesis. We consider genetic programming as a statistical inference problem and apply the Bayesian model-comparison framework to introduce a class of fitness functions with error and complexity terms. An adaptive learning method is then presented that automatically balances the model-complexity factor to evolve parsimonious programs without losing the diversity of the population needed for achieving the desired training accuracy. The effectiveness of this approach is empirically shown on the induction of sigma-pi neural networks for solving a real-world medical diagnosis problem as well as benchmark tasks. 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 #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 #248425. 	[Title] A Comparison of Selection Schemes used in Genetic Algorithms [Abstract] TIK-Report Nr. 11, December 1995 Version 2 (2. Edition) Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #227178. 	[Title] Evolving Visual Routines [Abstract] Traditional machine vision assumes that the vision system recovers a a complete, labeled description of the world [ Marr, 1982 ] . Recently, several researchers have criticized this model and proposed an alternative model which considers perception as a distributed collection of task-specific, task-driven visual routines [ Aloimonos, 1993, Ullman, 1987 ] . Some of these researchers have argued that in natural living systems these visual routines are the product of natural selection [ Ramachandran, 1985 ] . So far, researchers have hand-coded task-specific visual routines for actual implementations (e.g. [ Chapman, 1993 ] ). In this paper we propose an alternative approach in which visual routines for simple tasks are evolved using an artificial evolution approach. We present results from a series of runs on actual camera images, in which simple routines were evolved using Genetic Programming techniques [ Koza, 1992 ] . The results obtained are promising: the evolved routines are able to correctly classify up to 93% of the images, which is better than the best algorithm we were able to write by hand. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1128943. 	[Title] An Adverse Interaction between the Crossover Operator and a Restriction on Tree Depth of Crossover [Abstract] The Crossover operator is common to most implementations of Genetic Programming (GP). Another, usually unavoidable, factor is some form of restriction on the size of trees in the GP population. This paper concentrates on the interaction between the Crossover operator and a restriction on tree depth demonstrated by the MAX problem, which involves returning the largest possible value for given function and terminal sets. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #54129. 	[Title] Strongly Typed Genetic Programming [Abstract] BBN Technical Report #7866: Abstract Genetic programming is a powerful method for automatically generating computer programs via the process of natural selection [Koza 92]. However, it has the limitation known as "closure", i.e. that all the variables, constants, arguments for functions, and values returned from functions must be of the same data type. To correct this deficiency, we introduce a variation of genetic programming called "strongly typed" genetic programming (STGP). In STGP, variables, constants, arguments, and returned values can be of any data type with the provision that the data type for each such value be specified beforehand. This allows the initialization process and the genetic operators to only generate syntactically correct parse trees. Key concepts for STGP are generic functions, which are not true strongly typed functions but rather templates for classes of such functions, and generic data types, which are analogous. To illustrate STGP, we present four examples involving vector/matrix manipulation and list manipulation: (1) the multi-dimensional least-squares regression problem, (2) the multi-dimensional Kalman filter, (3) the list manipulation function NTH, and (4) the list manipulation function MAPCAR. 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.

Paper #54132 has the title `Causality in Genetic Programming  
` and the abstract `Causality relates changes in the structure of an object with the effects of such changes, that is changes in the properties or behavior of the object. This paper analyzes the concept of causality in Genetic Programming (GP) and suggests how it can be used in adapting control parameters for speeding up GP search. We first analyze the effects of crossover to show the weak causality of the GP representation and operators. Hierarchical GP approaches based on the discovery and evolution of functions amplify this phenomenon. However, selection gradually retains strongly causal changes. Causality is correlated to search space exploitation and is discussed in the context of the exploration-exploitation tradeoff. The results described argue for a bottom-up GP evolutionary thesis. Finally, new developments based on the idea of GP architecture evolution (Koza, 1994a) are discussed from the causality perspective. 
`.
Question: Which category should Paper #54132 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #54132 has the title `Causality in Genetic Programming  
` and the abstract `Causality relates changes in the structure of an object with the effects of such changes, that is changes in the properties or behavior of the object. This paper analyzes the concept of causality in Genetic Programming (GP) and suggests how it can be used in adapting control parameters for speeding up GP search. We first analyze the effects of crossover to show the weak causality of the GP representation and operators. Hierarchical GP approaches based on the discovery and evolution of functions amplify this phenomenon. However, selection gradually retains strongly causal changes. Causality is correlated to search space exploitation and is discussed in the context of the exploration-exploitation tradeoff. The results described argue for a bottom-up GP evolutionary thesis. Finally, new developments based on the idea of GP architecture evolution (Koza, 1994a) are discussed from the causality perspective. 
`.
Question: Which category should Paper #54132 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