Above are papers related to the following paper: [Paper_ID] Paper #58758. 	[Title] A Generalized Permutation Approach to Job Shop Scheduling with Genetic Algorithms [Abstract] In order to sequence the tasks of a job shop problem (JSP) on a number of machines related to the technological machine order of jobs, a new representation technique mathematically known as "permutation with repetition" is presented. The main advantage of this single chromosome representation is in analogy to the permutation scheme of the traveling salesman problem (TSP) that it cannot produce illegal sets of operation sequences (infeasible symbolic solutions). As a consequence of the representation scheme a new crossover operator preserving the initial scheme structure of permutations with repetition will be sketched. Its behavior is similar to the well known Order-Crossover for simple permutation schemes. Actually the GOX operator for permutations with repetition arises from a Generalisation of OX. Computational experiments show, that GOX passes the information from a couple of parent solutions efficiently to offspring solutions. Together, the new representation and GOX support the cooperative aspect of the genetic search for scheduling problems strongly. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1154176. 	[Title] operations: operation machine duration [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1128204. 	[Title] The Application of a Parallel Genetic Algorithm to the n=m=P=C max Flowshop Problem [Abstract] Hard combinatorial problems in sequencing and scheduling led recently into further research of genetic algorithms. Canonical coding of the symmetric TSP can be modified into a coding of the n-job m-machine flowshop problem, which configurates the solution space in a different way. We show that well known genetic operators act intelligently on this coding scheme. They implecitely prefer a subset of solutions which contain the probably best solutions with respect to an objective. We conjecture that every new problem needs a determination of this necessary condition for a genetic algorithm to work, i. e. a proof by experiment. We implemented an asynchronous parallel genetic algorithm on a UNIX-based computer network. Computational results of the new heuristic are discussed. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1128208. 	[Title] Genetic Algorithm based Scheduling in a Dynamic Manufacturing Environment [Abstract] The application of adaptive optimization strategies to scheduling in manufacturing systems has recently become a research topic of broad interest. Population based approaches to scheduling predominantly treat static data models, whereas real-world scheduling tends to be a dynamic problem. This paper briefly outlines the application of a genetic algorithm to the dynamic job shop problem arising in production scheduling. First we sketch a genetic algorithm which can handle release times of jobs. In a second step a preceding simulation method is used to improve the performance of the algorithm. Finally the job shop is regarded as a nondeterministic optimization problem arising from the occurrence of job releases. Temporal Decomposition leads to a scheduling control that interweaves both simulation in time and genetic search. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #593813. 	[Title] Scheduling Maintenance of Electrical Power Transmission Networks Using Genetic Programming [Abstract] Previous work showed the combination of a Genetic Algorithm using an order or permutation chromosome combined with hand coded "Greedy" Optimizers can readily produce an optimal schedule for a four node test problem [ Langdon, 1995 ] . Following this the same GA has been used to find low cost schedules for the South Wales region of the UK high voltage power network. This paper describes the evolution of the best known schedule for the base South Wales problem using Genetic Programming starting from the hand coded heuris tics used with the GA. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1688. 	[Title] Using Neural Networks and Genetic Algorithms as Heuristics for NP-Complete Problems [Abstract] Paradigms for using neural networks (NNs) and genetic algorithms (GAs) to heuristically solve boolean satisfiability (SAT) problems are presented. Since SAT is NP-Complete, any other NP-Complete problem can be transformed into an equivalent SAT problem in polynomial time, and solved via either paradigm. This technique is illustrated for hamiltonian circuit (HC) problems. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1133338. 	[Title] Vector Quantizer Design Using Genetic Algorithms [Abstract] A Genetic Algorithmic (GA) approach to vector quantizer design that combines the conventional Generalized Lloyd Algorithm (GLA) [6] is presented. We refer to this hybrid as the Genetic Generalized Lloyd Algorithm (GGLA). It works briefly as follows: A finite number of codebooks, called chromosomes, are selected. Each codebook undergoes iterative cycles of reproduction. We perform experiments with various alternative design choices using Gaussian-Markov processes, speech, and image as source data and signal-to-noise ratio (SNR) as the performance measure. In most cases, the GGLA showed performance improvements with respect to the GLA. We also compare our results with the Zador-Gersho formula [2, 9]. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1694. 	[Title] A NN Algorithm for Boolean Satisfiability Problems [Abstract] Satisfiability (SAT) refers to the task of finding a truth assignment that makes an arbitrary boolean expression true. This paper compares a neural network algorithm (NNSAT) with GSAT [4], a greedy algorithm for solving satisfiability problems. GSAT can solve problem instances that are difficult for traditional satisfiability algorithms. Results suggest that NNSAT scales better as the number of variables increase, solving at least as many hard SAT problems. 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 #152483. 	[Title] An Analysis of the Effects of Neighborhood Size and Shape on Local Selection Algorithms [Abstract] The increasing availability of finely-grained parallel architectures has resulted in a variety of evolutionary algorithms (EAs) in which the population is spatially distributed and local selection algorithms operate in parallel on small, overlapping neighborhoods. The effects of design choices regarding the particular type of local selection algorithm as well as the size and shape of the neighborhood are not particularly well understood and are generally tested empirically. In this paper we extend the techniques used to more formally analyze selection methods for sequential EAs and apply them to local neighborhood models, resulting in a much clearer understanding of the effects of neighborhood size and shape. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #69284. 	[Title] An Overview of Genetic Algorithms Part 1, Fundamentals [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #206371. 	[Title] Genetic Algorithms for Adaptive Planning of Path and Trajectory of a Mobile Robot in 2D Terrains [Abstract] This paper proposes genetic algorithms (GAs) for path planning and trajectory planning of an autonomous mobile robot. Our GA-based approach has an advantage of adaptivity such that the GAs work even if an environment is time-varying or unknown. Therefore, it is suitable for both off-line and on-line motion planning. We first presents a GA for path planning in a 2D terrain. Simulation results on the performance and adaptivity of the GA on randomly generated terrains are shown. Then, we discuss extensions of the GA for solving both path planning and trajectory planning simultaneously. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #82087. 	[Title] Fast Probabilistic Modeling for Combinatorial Optimization [Abstract] Probabilistic models have recently been utilized for the optimization of large combinatorial search problems. However, complex probabilistic models that attempt to capture inter-parameter dependencies can have prohibitive computational costs. The algorithm presented in this paper, termed COMIT, provides a method for using probabilistic models in conjunction with fast search techniques. We show how COMIT can be used with two very different fast search algorithms: hillclimbing and Population-based incremental learning (PBIL). The resulting algorithms maintain many of the benefits of probabilistic modeling, with far less computational expense. Extensive empirical results are provided; COMIT has been successfully applied to jobshop scheduling, traveling salesman, and knapsack problems. This paper also presents a review of probabilistic modeling for combi natorial optimization. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1153577. 	[Title]  [Abstract] Eugenic Evolution for Combinatorial Optimization John William Prior Report AI98-268 May 1998 Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1153065. 	[Title]  [Abstract] CBR Assisted Explanation of GA Results Computer Science Technical Report number 361 CRCC Technical Report number 63 Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1136814. 	[Title] A Comparative Study of Genetic Search [Abstract] We present a comparative study of genetic algorithms and their search properties when treated as a combinatorial optimization technique. This is done in the context of the NP-hard problem MAX-SAT, the comparison being relative to the Metropolis process, and by extension, simulated annealing. Our contribution is two-fold. First, we show that for large and difficult MAX-SAT instances, the contribution of cross-over to the search process is marginal. Little is lost if it is dispensed altogether, running mutation and selection as an enlarged Metropolis process. Second, we show that for these problem instances, genetic search consistently performs worse than simulated annealing when subject to similar resource bounds. The correspondence between the two algorithms is made more precise via a decomposition argument, and provides a framework for interpreting our results. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #69296. 	[Title] A Case Study on Tuning of Genetic Algorithms by Using Performance Evaluation Based on Experimental Design [Abstract] This paper proposes four performance measures of a genetic algorithm (GA) which enable us to compare different GAs for an op timization problem and different choices of their parameters' values. The performance measures are defined in terms of observations in simulation, such as the frequency of optimal solutions, fitness values, the frequency of evolution leaps, and the number of generations needed to reach an optimal solution. We present a case study in which parameters of a GA for robot path planning was tuned and its performance was optimized through performance evaluation by using the measures. Especially, one of the performance measures is used to demonstrate the adaptivity of the GA for robot path planning. We also propose a process of systematic tuning based on techniques for the design of experiments. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #39474. 	[Title] Simulated Annealing for Hard Satisfiability Problems [Abstract] Satisfiability (SAT) refers to the task of finding a truth assignment that makes an arbitrary boolean expression true. This paper compares a simulated annealing algorithm (SASAT) with GSAT (Selman et al., 1992), a greedy algorithm for solving satisfiability problems. GSAT can solve problem instances that are extremely difficult for traditional satisfiability algorithms. Results suggest that SASAT scales up better as the number of variables increases, solving at least as many hard SAT problems with less effort. The paper then presents an ablation study that helps to explain the relative advantage of SASAT over GSAT. Finally, an improvement to the basic SASAT algorithm is examined, based on a random walk suggested by Selman et al. (1993). Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #56115. 	[Title] Improving Tactical Plans with Genetic Algorithms [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #84021. 	[Title] Using Genetic Algorithms for Supervised Concept Learning [Abstract] Genetic Algorithms (GAs) have traditionally been used for non-symbolic learning tasks. In this chapter we consider the application of a GA to a symbolic learning task, supervised concept learning from examples. A GA concept learner (GABL) is implemented that learns a concept from a set of positive and negative examples. GABL is run in a batch-incremental mode to facilitate comparison with an incremental concept learner, ID5R. Preliminary results support that, despite minimal system bias, GABL is an effective concept learner and is quite competitive with ID5R as the target concept increases in complexity. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1134022. 	[Title] As mentioned in the introduction, an encod-ing/crossover pair makes a spectrum of geographical linkages. A [Abstract] It is open as to which chromosomal dimension performs best. Although higher-dimensional encodings (whether real or imaginary) can preserve more geographical gene linkages, we suspect that too high a dimension would not perform desirably. We are studying the question of which dimension of encoding is best for a given instance. It is likely that the optimal dimension is somehow dependent on the chromosome size and the input graph topology; interactions with the flexibility of crossover are yet unknown. The interaction of these considerations with the number of cuts used in the crossover is also an open issue. * In relocating genes onto a multi-dimensional chromosome, the simplest way is via a sequential assignment such as row-major order. Section 4 showed that performance improves when a DFS-row-major reembedding is used for two- and three-dimensional encodings. We suspect that this phenomenon will be consistent for higher-dimensional cases, and hope to perform more detailed investigations in the future. Although DFS reordering proved to be helpful for both linear encodings [19] and multi-dimensional encodings, we do not believe DFS-row-major reembedding is a good approach for the multi-dimensional cases since the row-major embedding is so simplistic. We are considering alternative 2-dimensional and 3-dimensional reembeddings which will hopefully provide further improvement. [4] T. N. Bui and B. R. Moon. Hyperplane synthesis for genetic algorithms. In Fifth International Conference on Genetic Algorithms, pages 102-109, July 1993. [5] T. N. Bui and B. R. Moon. Analyzing hyperplane synthesis in genetic algorithms using clustered schemata. In International Conference on Evolutionary Computation, Oct. 1994. Lecture Notes in Computer Science, 866:108-118, Springer-Verlag. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #576973. 	[Title] Control of Parallel Population Dynamics by Social-Like Behavior of GA-Individuals [Abstract] A frequently observed difficulty in the application of genetic algorithms to the domain of optimization arises from premature convergence. In order to preserve genotype diversity we develop a new model of auto-adaptive behavior for individuals. In this model a population member is an active individual that assumes social-like behavior patterns. Different individuals living in the same population can assume different patterns. By moving in a hierarchy of "social states" individuals change their behavior. Changes of social state are controlled by arguments of plausibility. These arguments are implemented as a rule set for a massively-parallel genetic algorithm. Computational experiments on 12 large-scale job shop benchmark problems show that the results of the new approach dominate the ordinary genetic algorithm significantly. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #44368. 	[Title] Using Problem Generators to Explore the Effects of Epistasis [Abstract] In this paper we develop an empirical methodology for studying the behavior of evolutionary algorithms based on problem generators. We then describe three generators that can be used to study the effects of epistasis on the performance of EAs. Finally, we illustrate the use of these ideas in a preliminary exploration of the effects of epistasis on simple GAs. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #576725. 	[Title] How good are genetic algorithms at finding large cliques: an experimental study [Abstract] This paper investigates the power of genetic algorithms at solving the MAX-CLIQUE problem. We measure the performance of a standard genetic algorithm on an elementary set of problem instances consisting of embedded cliques in random graphs. We indicate the need for improvement, and introduce a new genetic algorithm, the multi-phase annealed GA, which exhibits superior performance on the same problem set. As we scale up the problem size and test on "hard" benchmark instances, we notice a degraded performance in the algorithm caused by premature convergence to local minima. To alleviate this problem, a sequence of modifications are implemented ranging from changes in input representation to systematic local search. The most recent version, called union GA, incorporates the features of union cross-over, greedy replacement, and diversity enhancement. It shows a marked speed-up in the number of iterations required to find a given solution, as well as some improvement in the clique size found. We discuss issues related to the SIMD implementation of the genetic algorithms on a Thinking Machines CM-5, which was necessitated by the intrinsically high time complexity (O(n 3 )) of the serial algorithm for computing one iteration. Our preliminary conclusions are: (1) a genetic algorithm needs to be heavily customized to work "well" for the clique problem; (2) a GA is computationally very expensive, and its use is only recommended if it is known to find larger cliques than other algorithms; (3) although our customization effort is bringing forth continued improvements, there is no clear evidence, at this time, that a GA will have better success in circumventing local minima. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1103960. 	[Title] Surgery [Abstract] Object localization has applications in many areas of engineering and science. The goal is to spatially locate an arbitrarily-shaped object. In many applications, it is desirable to minimize the number of measurements collected for this purpose, while ensuring sufficient localization accuracy. In surgery, for example, collecting a large number of localization measurements may either extend the time required to perform a surgical procedure, or increase the radiation dosage to which a patient is exposed. Localization accuracy is a function of the spatial distribution of discrete measurements over an object when measurement noise is present. In [Simon et al., 1995a], metrics were presented to evaluate the information available from a set of discrete object measurements. In this study, new approaches to the discrete point data selection problem are described. These include hillclimbing, genetic algorithms (GAs), and Population-Based Incremental Learning (PBIL). Extensions of the standard GA and PBIL methods, which employ multiple parallel populations, are explored. The results of extensive empirical testing are provided. The results suggest that a combination of PBIL and hillclimbing result in the best overall performance. A computer-assisted surgical system which incorporates some of the methods presented in this paper is currently being evaluated in cadaver trials. Evolution-Based Methods for Selecting Point Data Shumeet Baluja was supported by a National Science Foundation Graduate Student Fellowship and a Graduate Student Fellowship from the National Aeronautics and Space Administration, administered by the Lyndon B. Johnson Space Center, Houston, TX. David Simon was partially supported by a National Science Foundation National Challenge grant (award IRI-9422734). for Object Localization: Applications to Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1131360. 	[Title] Solving Combinatorial Problems Using Evolutionary Algorithms [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #694759. 	[Title] An Evolutionary Approach to Vector Quantizer Design [Abstract] Vector quantization is a lossy coding technique for encoding a set of vectors from different sources such as image and speech. The design of vector quantizers that yields the lowest distortion is one of the most challenging problems in the field of source coding. However, this problem is known to be difficult [3]. The conventional solution technique works through a process of iterative refinements which yield only locally optimal results. In this paper, we design and evaluate three versions of genetic algorithms for computing vector quantizers. Our preliminary study with Gaussian-Markov sources showed that the genetic approach outperforms the conventional technique in most cases. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #33895. 	[Title] A Promising genetic Algorithm Approach to Job-Shop Scheduling, Rescheduling, and Open-Shop Scheduling Problems [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #33904. 	[Title] Stochastic Hillclimbing as a Baseline Method for Evaluating Genetic Algorithms [Abstract] We investigate the effectiveness of stochastic hillclimbing as a baseline for evaluating the performance of genetic algorithms (GAs) as combinatorial function optimizers. In particular, we address four problems to which GAs have been applied in the literature: the maximum cut problem, Koza's 11-multiplexer problem, MDAP (the Multiprocessor Document Allocation Problem), and the jobshop problem. We demonstrate that simple stochastic hillclimbing methods are able to achieve results comparable or superior to those obtained by the GAs designed to address these four problems. We further illustrate, in the case of the jobshop problem, how insights obtained in the formulation of a stochastic hillclimbing algorithm can lead to improvements in the encoding used by a GA. fl Department of Computer Science, University of California at Berkeley. Supported by a NASA Graduate Fellowship. This paper was written while the author was a visiting researcher at the Ecole Normale Superieure-rue d'Ulm, Groupe de BioInformatique, France. E-mail: juels@cs.berkeley.edu y Department of Mathematics, University of California at Berkeley. Supported by an NDSEG Graduate Fellowship. E-mail: wattenbe@math.berkeley.edu Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1103985. 	[Title] HGA: A Hardware-Based Genetic Algorithm [Abstract] A genetic algorithm (GA) is a robust problem-solving method based on natural selection. Hardware's speed advantage and its ability to parallelize offer great rewards to genetic al gorithms. Speedups of 1-3 orders of magnitude have been observed when frequently used software routines were im plemented in hardware by way of reprogrammable field-pro grammable gate arrays (FPGAs). Reprogrammability is es sential in a general-purpose GA engine because certain GA modules require changeability (e.g. the function to be opti mized by the GA). Thus a hardware-based GA is both feasi ble and desirable. A fully functional hardware-based genetic algorithm (the HGA) is presented here as a proof-of-concept system. It was designed using VHDL to allow for easy scala bility. It is designed to act as a coprocessor with the CPU of a PC. The user programs the FPGAs which implement the function to be optimized. Other GA parameters may also be specified by the user. Simulation results and performance analyses of the HGA are presented. A prototype HGA is de scribed and compared to a similar GA implemented in soft ware. In the simple tests, the prototype took about 6% as many clock cycles to run as the software-based GA. Further suggested improvements could realistically make the HGA 2-3 orders of magnitude faster than the software-based GA. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #647413. 	[Title] Solving 3-SAT by GAs Adapting Constraint Weights [Abstract] Handling NP complete problems with GAs is a great challenge. In particular the presence of constraints makes finding solutions hard for a GA. In this paper we present a problem independent constraint handling mechanism, Stepwise Adaptation of Weights (SAW), and apply it for solving the 3-SAT problem. Our experiments prove that the SAW mechanism substantially increases GA performance. Furthermore, we compare our SAW-ing GA with the best heuristic technique we could trace, WGSAT, and conclude that the GA is superior to the heuristic method. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #46079. 	[Title] A Sequential Niche Technique for Multimodal Function Optimization [Abstract] c fl UWCC COMMA Technical Report No. 93001, February 1993 x No part of this article may be reproduced for commercial purposes. Abstract A technique is described which allows unimodal function optimization methods to be extended to efficiently locate all optima of multimodal problems. We describe an algorithm based on a traditional genetic algorithm (GA). This involves iterating the GA, but uses knowledge gained during one iteration to avoid re-searching, on subsequent iterations, regions of problem space where solutions have already been found. This is achieved by applying a fitness derating function to the raw fitness function, so that fitness values are depressed in the regions of the problem space where solutions have already been found. Consequently, the likelihood of discovering a new solution on each iteration is dramatically increased. The technique may be used with various styles of GA, or with other optimization methods, such as simulated annealing. The effectiveness of the algorithm is demonstrated on a number of multimodal test functions. The technique is at least as fast as fitness sharing methods. It provides a speedup of between 1 and 10p on a problem with p optima, depending on the value of p and the convergence time complexity. Please summarise all the given information.

Paper #58758 has the title `A Generalized Permutation Approach to Job Shop Scheduling with Genetic Algorithms  
` and the abstract `In order to sequence the tasks of a job shop problem (JSP) on a number of machines related to the technological machine order of jobs, a new representation technique mathematically known as "permutation with repetition" is presented. The main advantage of this single chromosome representation is in analogy to the permutation scheme of the traveling salesman problem (TSP) that it cannot produce illegal sets of operation sequences (infeasible symbolic solutions). As a consequence of the representation scheme a new crossover operator preserving the initial scheme structure of permutations with repetition will be sketched. Its behavior is similar to the well known Order-Crossover for simple permutation schemes. Actually the GOX operator for permutations with repetition arises from a Generalisation of OX. Computational experiments show, that GOX passes the information from a couple of parent solutions efficiently to offspring solutions. Together, the new representation and GOX support the cooperative aspect of the genetic search for scheduling problems strongly. 
`.
Question: Which category should Paper #58758 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #58758 has the title `A Generalized Permutation Approach to Job Shop Scheduling with Genetic Algorithms  
` and the abstract `In order to sequence the tasks of a job shop problem (JSP) on a number of machines related to the technological machine order of jobs, a new representation technique mathematically known as "permutation with repetition" is presented. The main advantage of this single chromosome representation is in analogy to the permutation scheme of the traveling salesman problem (TSP) that it cannot produce illegal sets of operation sequences (infeasible symbolic solutions). As a consequence of the representation scheme a new crossover operator preserving the initial scheme structure of permutations with repetition will be sketched. Its behavior is similar to the well known Order-Crossover for simple permutation schemes. Actually the GOX operator for permutations with repetition arises from a Generalisation of OX. Computational experiments show, that GOX passes the information from a couple of parent solutions efficiently to offspring solutions. Together, the new representation and GOX support the cooperative aspect of the genetic search for scheduling problems strongly. 
`.
Question: Which category should Paper #58758 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