Above are papers related to the following paper: [Paper_ID] Paper #578669. 	[Title] Hybridized Crossover-Based Search Techniques for Program Discovery [Abstract] In this paper we address the problem of program discovery as defined by Genetic Programming [10]. We have two major results: First, by combining a hierarchical crossover operator with two traditional single point search algorithms: Simulated Annealing and Stochastic Iterated Hill Climbing, we have solved some problems with fewer fitness evaluations and a greater probability of a success than Genetic Programming. Second, we have managed to enhance Genetic Programming by hybridizing it with the simple scheme of hill climbing from a few individuals, at a fixed interval of generations. The new hill climbing component has two options for generating candidate solutions: mutation or crossover. When it uses crossover, mates are either randomly created, randomly drawn from the population at large, or drawn from a pool of fittest individuals. 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 #592993. 	[Title] Context Preserving Crossover in Genetic Programming. [Abstract] This paper introduces two new crossover operators for Genetic Programming (GP). Contrary to the regular GP crossover, the operators presented attempt to preserve the context in which subtrees appeared in the parent trees. A simple coordinate scheme for nodes in an S-expression tree is proposed, and crossovers are only allowed between nodes with exactly or partially matching coordinates. 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 #592996. 	[Title] Hierarchical Genetic Programming (HGP) extensions discover, modify, and exploit subroutines to accelerate the evolution of [Abstract] A fundamental problem in learning from observation and interaction with an environment is defining a good representation, that is a representation which captures the underlying structure and functionality of the domain. This chapter discusses an extension of the genetic programming (GP) paradigm based on the idea that subroutines obtained from blocks of good representations act as building blocks and may enable a faster evolution of even better representations. This GP extension algorithm is called adaptive representation through learning (ARL). It has built-in mechanisms for (1) creation of new subroutines through discovery and generalization of blocks of code; (2) deletion of subroutines. The set of evolved subroutines extracts common knowledge emerging during the evolutionary process and acquires the necessary structure for solving the problem. ARL was successfully tested on the problem of controlling an agent in a dynamic and non-deterministic environment. Results with the automatic discovery of subroutines show the potential to better scale up the GP technique to complex problems. While HGP approaches improve the efficiency and scalability of genetic programming (GP) for many applications [Koza, 1994b], several issues remain unresolved. The scalability of HGP techniques could be further improved by solving two such issues. One is the characterization of the value of subroutines. Current methods for HGP do not attempt to decide what is relevant, i.e. which blocks of code or subroutines may be worth giving special attention, but employ genetic operations on subroutines at random points. The other issue is the time-course of the generation of new subroutines. Current HGP techniques do not make informed choices to automatically decide when creation or modification of subroutines is advantageous or necessary. The Adaptive Representation through Learning (ARL) algorithm copes with both of these problems. The what issue is addressed by relying on local measures such as parent-offspring differential fitness and block activation in order to discover useful subroutines and by learning which subroutines are useful. The when issue is addressed by relying on global population measures such as population entropy in order to predict when search reaches local optima and escape them. ARL co-evolves a set of subroutines which extends the set of problem primitives. 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 #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 #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 #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 #595157. 	[Title] Investigating the Generality of Automatically Defined Functions [Abstract] This paper studies how well the combination of simulated annealing and ADFs solves genetic programming (GP) style program discovery problems. On a suite composed of the even-k-parity problems for k = 3,4,5, it analyses the performance of simulated annealing with ADFs as compared to not using ADFs. In contrast to GP results on this suite, when simulated annealing is run with ADFs, as problem size increases, the advantage to using them over a standard GP program representation is marginal. When the performance of simulated annealing is compared to GP with both algorithm using ADFs on the even-3-parity problem GP is advantageous, on the even-4-parity problem SA and GP are equal, and on the even-5-parity problem SA is advantageous. Please summarise all the given information.

Paper #578669 has the title `Hybridized Crossover-Based Search Techniques for Program Discovery  
` and the abstract `In this paper we address the problem of program discovery as defined by Genetic Programming [10]. We have two major results: First, by combining a hierarchical crossover operator with two traditional single point search algorithms: Simulated Annealing and Stochastic Iterated Hill Climbing, we have solved some problems with fewer fitness evaluations and a greater probability of a success than Genetic Programming. Second, we have managed to enhance Genetic Programming by hybridizing it with the simple scheme of hill climbing from a few individuals, at a fixed interval of generations. The new hill climbing component has two options for generating candidate solutions: mutation or crossover. When it uses crossover, mates are either randomly created, randomly drawn from the population at large, or drawn from a pool of fittest individuals.
`.
Question: Which category should Paper #578669 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #578669 has the title `Hybridized Crossover-Based Search Techniques for Program Discovery  
` and the abstract `In this paper we address the problem of program discovery as defined by Genetic Programming [10]. We have two major results: First, by combining a hierarchical crossover operator with two traditional single point search algorithms: Simulated Annealing and Stochastic Iterated Hill Climbing, we have solved some problems with fewer fitness evaluations and a greater probability of a success than Genetic Programming. Second, we have managed to enhance Genetic Programming by hybridizing it with the simple scheme of hill climbing from a few individuals, at a fixed interval of generations. The new hill climbing component has two options for generating candidate solutions: mutation or crossover. When it uses crossover, mates are either randomly created, randomly drawn from the population at large, or drawn from a pool of fittest individuals.
`.
Question: Which category should Paper #578669 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