Above are papers related to the following paper: [Paper_ID] Paper #1128975. 	[Title] learning easier tasks. More work is necessary in order to determine more precisely the relationship [Abstract] We have attempted to obtain a stronger correlation between the relationship between G 0 and G 1 and performance. This has included studying the variance in the fitnesses of the members of the population, as well as observing the rate of convergence of the GP with respect to G 1 when a population was evolved for G 0 . 13 Unfortunately, we have not yet been able to obtain a significant correlation. In future work, we plan to to track the genetic diversity (we have only considered phenotypic variance so far) of populations in order to shed some light on the underlying mechanism for priming. One factor that has made this analysis difficult so far is our use of genetic programming, for which the space of genotypes is very large, (i.e., there are many redundant solutions), and for which the neighborhood structure is less easily intuited than that of a standard genetic algorithm. Since there is every reason to believe that the underlying mechanism of incremental evolution is largely independent of the peculiarities of genetic programming, we are currently investigating the incremental evolution mechanism using genetic algorithms with fixed-length genotypes. This should enable a better understanding of the mechanism. Ultimately, we will scale up this research effort to analyze incremental evolution with more than one transition between test cases. This will involve many open issues regarding the optimization of the transition schedule between test cases. 13 We performed the following experiment: Let F it(I; G) be the fitness value of a genetic program I according to the evaluation function G, and Best Of(P op; t; G) be the member I fl of population P op at time t with highest fitness according to G | in other words, I fl = Best Of (P op; t; G) maximizes F it(I; G) over all I 2 P op. A population P op 0 was evolved in the usual manner using evaluation function G 0 for t = 25 generations. However, at each generation 1 i 25 we also evaluated the current population using evaluation function G 1 , and recorded the value of F it(Best Of (P op; i; G 1 ); G 1 ). In other words, we evolved the population using G 0 as the evaluation function, but at every generation we also computed the fitness of the best individual in the population according to G 1 and saved this value. Using the same random seed and control parameters, we then evolved a population P op 1 for t = 30 generations using G 1 as the evaluation function (note that at generation 0, P op 1 is identical to P op 0 ). For all values of t, we compared F it(Best Of (P op 0 ; t; G 1 ); G 1 ) with F it(Best Of (P op 1 ; t; G 1 ); G 1 ). in order to better formalize and exploit this notion of domain difficulty. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #12576. 	[Title] Learning Sequential Decision Rules Using Simulation Models and Competition [Abstract] The problem of learning decision rules for sequential tasks is addressed, focusing on the problem of learning tactical decision rules from a simple flight simulator. The learning method relies on the notion of competition and employs genetic algorithms to search the space of decision policies. Several experiments are presented that address issues arising from differences between the simulation model on which learning occurs and the target environment on which the decision rules are ultimately tested. 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 #1128997. 	[Title] Stochastic Random or probabilistic but with some direction. For example the arrival of people at [Abstract] Simulated Annealing Search technique where a single trial solution is modified at random. An energy is defined which represents how good the solution is. The goal is to find the best solution by minimising the energy. Changes which lead to a lower energy are always accepted; an increase is probabilistically accepted. The probability is given by exp(E=k B T ). Where E is the change in energy, k B is a constant and T is the Temperature. Initially the temperature is high corresponding to a liquid or molten state where large changes are possible and it is progressively reduced using a cooling schedule so allowing smaller changes until the system solidifies at a low energy solution. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #35852. 	[Title] Generalization and scaling in reinforcement learning [Abstract] In associative reinforcement learning, an environment generates input vectors, a learning system generates possible output vectors, and a reinforcement function computes feedback signals from the input-output pairs. The task is to discover and remember input-output pairs that generate rewards. Especially difficult cases occur when rewards are rare, since the expected time for any algorithm can grow exponentially with the size of the problem. Nonetheless, if a reinforcement function possesses regularities, and a learning algorithm exploits them, learning time can be reduced below that of non-generalizing algorithms. This paper describes a neural network algorithm called complementary reinforcement back-propagation (CRBP), and reports simulation results on problems designed to offer differing opportunities for generalization. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1112911. 	[Title] Evolution of Iteration in Genetic Programming D a v d A The solution to many [Abstract] This paper introduces the new operation of restricted iteration creation that automatically Genetic programming extends Holland's genetic algorithm to the task of automatic programming. Early work on genetic programming demonstrated that it is possible to evolve a sequence of work-performing steps in a single result-producing branch (that is, a one-part "main" program). The book Genetic Programming: On the Programming of Computers by Means of Natural Selection (Koza 1992) describes an extension of Holland's genetic algorithm in which the genetic population consists of computer programs (that is, compositions of primitive functions and terminals). See also Koza and Rice (1992). In the most basic form of genetic programming (where only a single result-producing branch is evolved), genetic programming demonstrated the capability to discover a sequence (as to both its length and its content) of work-performing steps that is sufficient to produce a satisfactory solution to several problems, including many problems that have been used over the years as benchmarks in machine learning and artificial intelligence. Before applying genetic programming to a problem, the user must perform five major preparatory steps, namely identifying the terminals (inputs) of the to-be-evolved programs, identifying the primitive functions (operations) contained in the to-be-evolved programs, creating the fitness measure for evaluating how well a given program does at solving the problem at hand, choosing certain control parameters (notably population size and number of generations to be run), and determining the termination criterion and method of result designation (typically the best-so-far individual from the populations produced during the run). creates a restricted iteration-performing Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #575795. 	[Title] Adapting the Evaluation Space to Improve Global Learning [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #35061. 	[Title] Evolving Networks: Using the Genetic Algorithm with Connectionist Learning [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1136342. 	[Title] The Evolution of Memory and Mental Models Using Genetic Programming build internal representations of their [Abstract] This paper applies genetic programming their successive actions. The results show to the evolution of intelligent agents that 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 #486840. 	[Title] Adaptation in constant utility non-stationary environments [Abstract] Environments that vary over time present a fundamental problem to adaptive systems. Although in the worst case there is no hope of effective adaptation, some forms environmental variability do provide adaptive opportunities. We consider a broad class of non-stationary environments, those which combine a variable result function with an invariant utility function, and demonstrate via simulation that an adaptive strategy employing both evolution and learning can tolerate a much higher rate of environmental variation than an evolution-only strategy. We suggest that in many cases where stability has previously been assumed, the constant utility non-stationary environment may in fact be a more powerful viewpoint. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #595063. 	[Title] Improving the Performance of Evolutionary Optimization by Dynamically Scaling the Evaluation Function [Abstract] Traditional evolutionary optimization algorithms assume a static evaluation function, according to which solutions are evolved. Incremental evolution is an approach through which a dynamic evaluation function is scaled over time in order to improve the performance of evolutionary optimization. In this paper, we present empirical results that demonstrate the effectiveness of this approach for genetic programming. Using two domains, a two-agent pursuit-evasion game and the Tracker [6] trail-following task, we demonstrate that incremental evolution is most successful when applied near the beginning of an evolutionary run. We also show that incremental evolution can be successful when the intermediate evaluation functions are more difficult than the target evaluation function, as well as when they are easier than the target function. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #144701. 	[Title] Evolution of Mapmaking: Learning, planning, and memory using Genetic Programming [Abstract] An essential component of an intelligent agent is the ability to observe, encode, and use information about its environment. Traditional approaches to Genetic Programming have focused on evolving functional or reactive programs with only a minimal use of state. This paper presents an approach for investigating the evolution of learning, planning, and memory using Genetic Programming. The approach uses a multi-phasic fitness environment that enforces the use of memory and allows fairly straightforward comprehension of the evolved representations . An illustrative problem of 'gold' collection is used to demonstrate the usefulness of the approach. The results indicate that the approach can evolve programs that store simple representations of their environments and use these representations to produce simple plans. Please summarise all the given information.

Paper #1128975 has the title `learning easier tasks. More work is necessary in order to determine more precisely the relationship
` and the abstract `We have attempted to obtain a stronger correlation between the relationship between G 0 and G 1 and performance. This has included studying the variance in the fitnesses of the members of the population, as well as observing the rate of convergence of the GP with respect to G 1 when a population was evolved for G 0 . 13 Unfortunately, we have not yet been able to obtain a significant correlation. In future work, we plan to to track the genetic diversity (we have only considered phenotypic variance so far) of populations in order to shed some light on the underlying mechanism for priming. One factor that has made this analysis difficult so far is our use of genetic programming, for which the space of genotypes is very large, (i.e., there are many redundant solutions), and for which the neighborhood structure is less easily intuited than that of a standard genetic algorithm. Since there is every reason to believe that the underlying mechanism of incremental evolution is largely independent of the peculiarities of genetic programming, we are currently investigating the incremental evolution mechanism using genetic algorithms with fixed-length genotypes. This should enable a better understanding of the mechanism. Ultimately, we will scale up this research effort to analyze incremental evolution with more than one transition between test cases. This will involve many open issues regarding the optimization of the transition schedule between test cases. 13 We performed the following experiment: Let F it(I; G) be the fitness value of a genetic program I according to the evaluation function G, and Best Of(P op; t; G) be the member I fl of population P op at time t with highest fitness according to G | in other words, I fl = Best Of (P op; t; G) maximizes F it(I; G) over all I 2 P op. A population P op 0 was evolved in the usual manner using evaluation function G 0 for t = 25 generations. However, at each generation 1 i 25 we also evaluated the current population using evaluation function G 1 , and recorded the value of F it(Best Of (P op; i; G 1 ); G 1 ). In other words, we evolved the population using G 0 as the evaluation function, but at every generation we also computed the fitness of the best individual in the population according to G 1 and saved this value. Using the same random seed and control parameters, we then evolved a population P op 1 for t = 30 generations using G 1 as the evaluation function (note that at generation 0, P op 1 is identical to P op 0 ). For all values of t, we compared F it(Best Of (P op 0 ; t; G 1 ); G 1 ) with F it(Best Of (P op 1 ; t; G 1 ); G 1 ). in order to better formalize and exploit this notion of domain difficulty.
`.
Question: Which category should Paper #1128975 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #1128975 has the title `learning easier tasks. More work is necessary in order to determine more precisely the relationship
` and the abstract `We have attempted to obtain a stronger correlation between the relationship between G 0 and G 1 and performance. This has included studying the variance in the fitnesses of the members of the population, as well as observing the rate of convergence of the GP with respect to G 1 when a population was evolved for G 0 . 13 Unfortunately, we have not yet been able to obtain a significant correlation. In future work, we plan to to track the genetic diversity (we have only considered phenotypic variance so far) of populations in order to shed some light on the underlying mechanism for priming. One factor that has made this analysis difficult so far is our use of genetic programming, for which the space of genotypes is very large, (i.e., there are many redundant solutions), and for which the neighborhood structure is less easily intuited than that of a standard genetic algorithm. Since there is every reason to believe that the underlying mechanism of incremental evolution is largely independent of the peculiarities of genetic programming, we are currently investigating the incremental evolution mechanism using genetic algorithms with fixed-length genotypes. This should enable a better understanding of the mechanism. Ultimately, we will scale up this research effort to analyze incremental evolution with more than one transition between test cases. This will involve many open issues regarding the optimization of the transition schedule between test cases. 13 We performed the following experiment: Let F it(I; G) be the fitness value of a genetic program I according to the evaluation function G, and Best Of(P op; t; G) be the member I fl of population P op at time t with highest fitness according to G | in other words, I fl = Best Of (P op; t; G) maximizes F it(I; G) over all I 2 P op. A population P op 0 was evolved in the usual manner using evaluation function G 0 for t = 25 generations. However, at each generation 1 i 25 we also evaluated the current population using evaluation function G 1 , and recorded the value of F it(Best Of (P op; i; G 1 ); G 1 ). In other words, we evolved the population using G 0 as the evaluation function, but at every generation we also computed the fitness of the best individual in the population according to G 1 and saved this value. Using the same random seed and control parameters, we then evolved a population P op 1 for t = 30 generations using G 1 as the evaluation function (note that at generation 0, P op 1 is identical to P op 0 ). For all values of t, we compared F it(Best Of (P op 0 ; t; G 1 ); G 1 ) with F it(Best Of (P op 1 ; t; G 1 ); G 1 ). in order to better formalize and exploit this notion of domain difficulty.
`.
Question: Which category should Paper #1128975 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