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 #116545. 	[Title] Duplication of Coding Segments in Genetic Programming [Abstract] Research into the utility of non-coding segments, or introns, in genetic-based encodings has shown that they expedite the evolution of solutions in domains by protecting building blocks against destructive crossover. We consider a genetic programming system where non-coding segments can be removed, and the resultant chromosomes returned into the population. This parsimonious repair leads to premature convergence, since as we remove the naturally occurring non-coding segments, we strip away their protective backup feature. We then duplicate the coding segments in the repaired chromosomes, and place the modified chromosomes into the population. The duplication method significantly improves the learning rate in the domain we have considered. We also show that this method can be applied to other domains. 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 #116552. 	[Title] Empirical studies of the genetic algorithm with non-coding segments [Abstract] The genetic algorithm (GA) is a problem solving method that is modelled after the process of natural selection. We are interested in studying a specific aspect of the GA: the effect of non-coding segments on GA performance. Non-coding segments are segments of bits in an individual that provide no contribution, positive or negative, to the fitness of that individual. Previous research on non-coding segments suggests that including these structures in the GA may improve GA performance. Understanding when and why this improvement occurs will help us to use the GA to its full potential. In this article, we discuss our hypotheses on non-coding segments and describe the results of our experiments. The experiments may be separated into two categories: testing our program on problems from previous related studies, and testing new hypotheses on the effect of non-coding segments. 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 #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 #116553. 	[Title] A Survey of Intron Research in Genetics [Abstract] A brief survey of biological research on non-coding DNA is presented here. There has been growing interest in the effects of non-coding segments in evolutionary algorithms (EAs). To better understand and conduct research on non-coding segments and EAs, it is important to understand the biological background of such work. This paper begins with a review of basic genetics and terminology, describes the different types of non-coding DNA, and then surveys recent intron research. Please summarise all the given information.

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 #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 #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 #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 #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 #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.

Paper #1113742 has the title `Testing the Robustness of the Genetic Algorithm on the Floating Building Block Representation.  
` and the 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. 
`.
Question: Which category should Paper #1113742 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #1113742 has the title `Testing the Robustness of the Genetic Algorithm on the Floating Building Block Representation.  
` and the 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. 
`.
Question: Which category should Paper #1113742 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