Above are papers related to the following paper: [Paper_ID] Paper #110041. 	[Title] Generation Gaps Revisited [Abstract] There has been a lot of recent interest in so-called "steady state" genetic algorithms (GAs) which, among other things, replace only a few individuals (typically 1 or 2) each generation from a fixed size population of size N. Understanding the advantages and/or disadvantages of replacing only a fraction of the population each generation (rather than the entire population) was a goal of some of the earliest GA research. In spite of considerable progress in our understanding of GAs since then, the pros/cons of overlapping generations remains a somewhat cloudy issue. However, recent theoretical and empirical results provide the background for a much clearer understanding of this issue. In this paper we review, combine, and extend these results in a way that significantly sharpens our insight. 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 #98693. 	[Title] A STUDY OF CROSSOVER OPERATORS IN GENETIC PROGRAMMING [Abstract] Holland's analysis of the sources of power of genetic algorithms has served as guidance for the applications of genetic algorithms for more than 15 years. The technique of applying a recombination operator (crossover) to a population of individuals is a key to that power. Neverless, there have been a number of contradictory results concerning crossover operators with respect to overall performance. Recently, for example, genetic algorithms were used to design neural network modules and their control circuits. In these studies, a genetic algorithm without crossover outperformed a genetic algorithm with crossover. This report re-examines these studies, and concludes that the results were caused by a small population size. New results are presented that illustrate the effectiveness of crossover when the population size is larger. From a performance view, the results indicate that better neural networks can be evolved in a shorter time if the genetic algorithm uses crossover. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1152358. 	[Title] Crossover or Mutation? [Abstract] Genetic algorithms rely on two genetic operators crossover and mutation. Although there exists a large body of conventional wisdom concerning the roles of crossover and mutation, these roles have not been captured in a theoretical fashion. For example, it has never been theoretically shown that mutation is in some sense "less powerful" than crossover or vice versa. This paper provides some answers to these questions by theoretically demonstrating that there are some important characteristics of each operator that are not captured by the other. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #82920. 	[Title] A Survey of Evolution Strategies [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1130856. 	[Title] Chaos, Fractals, and Genetic Algorithms [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #287787. 	[Title] LIBGA: A USER-FRIENDLY WORKBENCH FOR ORDER-BASED GENETIC ALGORITHM RESEARCH [Abstract] Over the years there has been several packages developed that provide a workbench for genetic algorithm (GA) research. Most of these packages use the generational model inspired by GENESIS. A few have adopted the steady-state model used in Genitor. Unfortunately, they have some deficiencies when working with order-based problems such as packing, routing, and scheduling. This paper describes LibGA, which was developed specifically for order-based problems, but which also works easily with other kinds of problems. It offers an easy to use `user-friendly' interface and allows comparisons to be made between both generational and steady-state genetic algorithms for a particular problem. It includes a variety of genetic operators for reproduction, crossover, and mutation. LibGA makes it easy to use these operators in new ways for particular applications or to develop and include new operators. Finally, it offers the unique new feature of a dynamic generation gap. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1123756. 	[Title] Case-Based Probability Factoring in Bayesian Belief Networks [Abstract] Bayesian network inference can be formulated as a combinatorial optimization problem, concerning in the computation of an optimal factoring for the distribution represented in the net. Since the determination of an optimal factoring is a computationally hard problem, heuristic greedy strategies able to find approximations of the optimal factoring are usually adopted. In the present paper we investigate an alternative approach based on a combination of genetic algorithms (GA) and case-based reasoning (CBR). We show how the use of genetic algorithms can improve the quality of the computed factoring in case a static strategy is used (as for the MPE computation), while the combination of GA and CBR can still provide advantages in the case of dynamic strategies. Some preliminary results on different kinds of nets are then reported. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #503883. 	[Title] Genetic algorithms with multi-parent recombination [Abstract] In this paper we investigate genetic algorithms where more than two parents are involved in the recombination operation. In particular, we introduce gene scanning as a reproduction mechanism that generalizes classical crossovers, such as n-point crossover or uniform crossover, and is applicable to an arbitrary number (two or more) of parents. We performed extensive tests for optimizing numerical functions, the TSP and graph coloring to observe the effect of different numbers of parents. The experiments show that 2-parent recombination is outperformed when using more parents on the classical DeJong functions. For the other problems the results are not conclusive, in some cases 2 parents are optimal, while in some others more parents are better. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #97390. 	[Title] On the Virtues of Parameterized Uniform Crossover [Abstract] Traditionally, genetic algorithms have relied upon 1 and 2-point crossover operators. Many recent empirical studies, however, have shown the benefits of higher numbers of crossover points. Some of the most intriguing recent work has focused on uniform crossover, which involves on the average L/2 crossover points for strings of length L. Theoretical results suggest that, from the view of hyperplane sampling disruption, uniform crossover has few redeeming features. However, a growing body of experimental evidence suggests otherwise. In this paper, we attempt to reconcile these opposing views of uniform crossover and present a framework for understanding its virtues. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #14062. 	[Title] An Analysis of the Interacting Roles of Population Size and Crossover in Genetic Algorithms [Abstract] In this paper we present some theoretical and empirical results on the interacting roles of population size and crossover in genetic algorithms. We summarize recent theoretical results on the disruptive effect of two forms of multi-point crossover: n-point crossover and uniform crossover. We then show empirically that disruption analysis alone is not sufficient for selecting appropriate forms of crossover. However, by taking into account the interacting effects of population size and crossover, a general picture begins to emerge. The implications of these results on implementation issues and performance are discussed, and several directions for further research are suggested. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #415693. 	[Title] Using Real-Valued Genetic Algorithms to Evolve Rule Sets for Classification [Abstract] In this paper, we use a genetic algorithm to evolve a set of classification rules with real-valued attributes. We show how real-valued attribute ranges can be encoded with real-valued genes and present a new uniform method for representing don't cares in the rules. We view supervised classification as an optimization problem, and evolve rule sets that maximize the number of correct classifications of input instances. We use a variant of the Pitt approach to genetic-based machine learning system with a novel conflict resolution mechanism between competing rules within the same rule set. Experimental results demonstrate the effectiveness of our proposed approach on a benchmark wine classifier system. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #634902. 	[Title] Heuristic for Improved Genetic Bin Packing [Abstract] University of Tulsa Technical Report UTULSA-MCS-93-8, May, 1993. Submitted to Information Processing Letters, May, 1993. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #647447. 	[Title] Performance of Multi-Parent Crossover Operators on Numerical Function Optimization Problems [Abstract] The multi-parent scanning crossover, generalizing the traditional uniform crossover, and diagonal crossover, generalizing 1-point (n-point) crossovers, were introduced in [5]. In subsequent publications, see [6, 18, 19], several aspects of multi-parent recombination are discussed. Due to space limitations, however, a full overview of experimental results showing the performance of multi-parent GAs on numerical optimization problems has never been published. This technical report is meant to fill this gap and make results available. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #634904. 	[Title] A PARALLEL ISLAND MODEL GENETIC ALGORITHM FOR THE MULTIPROCESSOR SCHEDULING PROBLEM [Abstract] In this paper we compare the performance of a serial and a parallel island model Genetic Algorithm for solving the Multiprocessor Scheduling Problem. We show results using fixed and scaled problems both using and not using migration. We have found that in addition to providing a speedup through the use of parallel processing, the parallel island model GA with migration finds better quality solutions than the serial GA. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #634938. 	[Title] A GENETIC ALGORITHM FOR FRAGMENT ALLOCATION IN A DISTRIBUTED DATABASE SYSTEM [Abstract] In this paper we explore the distributed database allocation problem, which is intractable. We also discuss genetic algorithms and how they have been used successfully to solve combinatorial problems. Our experimental results show the GA to be far superior to the greedy heuristic in obtaining optimal and near optimal fragment placements for the allocation problem with various data sets. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #634975. 	[Title] TECHNIQUES FOR REDUCING THE DISRUPTION OF SUPERIOR BUILDING BLOCKS IN GENETIC ALGORITHMS [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1130847. 	[Title] A Genetic Algorithm for File and Task Placement in a Distributed System [Abstract] In this paper we explore the distributed file and task placement problem, which is intractable. We also discuss genetic algorithms and how they have been used successfully to solve combinatorial problems. Our experimental results show the GA to be far superior to the greedy heuristic in obtaining optimal and near optimal file and task placements for the problem with various data sets. Please summarise all the given information.

Paper #110041 has the title `Generation Gaps Revisited  
` and the abstract `There has been a lot of recent interest in so-called "steady state" genetic algorithms (GAs) which, among other things, replace only a few individuals (typically 1 or 2) each generation from a fixed size population of size N. Understanding the advantages and/or disadvantages of replacing only a fraction of the population each generation (rather than the entire population) was a goal of some of the earliest GA research. In spite of considerable progress in our understanding of GAs since then, the pros/cons of overlapping generations remains a somewhat cloudy issue. However, recent theoretical and empirical results provide the background for a much clearer understanding of this issue. In this paper we review, combine, and extend these results in a way that significantly sharpens our insight.
`.
Question: Which category should Paper #110041 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #110041 has the title `Generation Gaps Revisited  
` and the abstract `There has been a lot of recent interest in so-called "steady state" genetic algorithms (GAs) which, among other things, replace only a few individuals (typically 1 or 2) each generation from a fixed size population of size N. Understanding the advantages and/or disadvantages of replacing only a fraction of the population each generation (rather than the entire population) was a goal of some of the earliest GA research. In spite of considerable progress in our understanding of GAs since then, the pros/cons of overlapping generations remains a somewhat cloudy issue. However, recent theoretical and empirical results provide the background for a much clearer understanding of this issue. In this paper we review, combine, and extend these results in a way that significantly sharpens our insight.
`.
Question: Which category should Paper #110041 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