Above are papers related to the following paper: [Paper_ID] Paper #1128201. 	[Title] A DISCUSSION ON SOME DESIGN PRINCIPLES FOR EFFICIENT CROSSOVER OPERATORS FOR GRAPH COLORING PROBLEMS [Abstract] A year ago, a new metaheuristic for graph coloring problems was introduced by Costa, Hertz and Dubuis. They have shown, with computer experiments, some clear indication of the benefits of this approach. Graph coloring has many applications specially in the areas of scheduling, assignments and timetabling. The metaheuristic can be classified as a memetic algorithm since it is based on a population search in which periods of local optimization are interspersed with phases in which new configurations are created from earlier well-developed configurations or local minima of the previous iterative improvement process. The new population is created using crossover operators as in genetic algorithms. In this paper we discuss how a methodology inspired in Competitive Analysis may be relevant to the problem of designing better crossover operators. RESUMO: No ultimo ano uma nova metaheurstica para o problema de colora~c~ao em grafos foi apre-sentada por Costa, Hertz e Dubuis. Eles mostraram, com experimentos computacionais, algumas indica~c~oes claras dos benefcios desta nova tecnica. Colora~c~ao em grafos tem muitas aplica~c~oes, especialmente na area de programa~c~ao de tarefas, localiza~c~ao e horario . A metaheurstica pode ser classificada como algoritmo memetico desde que seja baseada em uma busca de popula~c~ao cujos perodos de otimiza~c~ao local s~ao intercalados com fases onde novas configura~c~oes s~ao criadas a partir de boas configura~c~oes ou mnimos locais de itera~c~oes anteriores. A nova popula~c~ao e criada usando opera~c~oes de crossover como em algoritmos geneticos. Neste artigo apresen-tamos como uma metodologia baseada em Competitive Analysis pode ser relevante para construir opera~c~oes de crossover. 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 #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 #574264. 	[Title] An evolutionary tabu search algorithm and the NHL scheduling problem [Abstract] We present in this paper a new evolutionary procedure for solving general optimization problems that combines efficiently the mechanisms of genetic algorithms and tabu search. In order to explore the solution space properly interaction phases are interspersed with periods of optimization in the algorithm. An adaptation of this search principle to the National Hockey League (NHL) problem is discussed. The hybrid method developed in this paper is well suited for Open Shop Scheduling problems (OSSP). The results obtained appear to be quite satisfactory. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #576795. 	[Title] Embedding of a sequential procedure within an evolutionary algorithm for coloring problems in graphs [Abstract]  Please summarise all the given information.

Paper #1128201 has the title `A DISCUSSION ON SOME DESIGN PRINCIPLES FOR EFFICIENT CROSSOVER OPERATORS FOR GRAPH COLORING PROBLEMS  
` and the abstract `A year ago, a new metaheuristic for graph coloring problems was introduced by Costa, Hertz and Dubuis. They have shown, with computer experiments, some clear indication of the benefits of this approach. Graph coloring has many applications specially in the areas of scheduling, assignments and timetabling. The metaheuristic can be classified as a memetic algorithm since it is based on a population search in which periods of local optimization are interspersed with phases in which new configurations are created from earlier well-developed configurations or local minima of the previous iterative improvement process. The new population is created using crossover operators as in genetic algorithms. In this paper we discuss how a methodology inspired in Competitive Analysis may be relevant to the problem of designing better crossover operators. RESUMO: No ultimo ano uma nova metaheurstica para o problema de colora~c~ao em grafos foi apre-sentada por Costa, Hertz e Dubuis. Eles mostraram, com experimentos computacionais, algumas indica~c~oes claras dos benefcios desta nova tecnica. Colora~c~ao em grafos tem muitas aplica~c~oes, especialmente na area de programa~c~ao de tarefas, localiza~c~ao e horario . A metaheurstica pode ser classificada como algoritmo memetico desde que seja baseada em uma busca de popula~c~ao cujos perodos de otimiza~c~ao local s~ao intercalados com fases onde novas configura~c~oes s~ao criadas a partir de boas configura~c~oes ou mnimos locais de itera~c~oes anteriores. A nova popula~c~ao e criada usando opera~c~oes de crossover como em algoritmos geneticos. Neste artigo apresen-tamos como uma metodologia baseada em Competitive Analysis pode ser relevante para construir opera~c~oes de crossover. 
`.
Question: Which category should Paper #1128201 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #1128201 has the title `A DISCUSSION ON SOME DESIGN PRINCIPLES FOR EFFICIENT CROSSOVER OPERATORS FOR GRAPH COLORING PROBLEMS  
` and the abstract `A year ago, a new metaheuristic for graph coloring problems was introduced by Costa, Hertz and Dubuis. They have shown, with computer experiments, some clear indication of the benefits of this approach. Graph coloring has many applications specially in the areas of scheduling, assignments and timetabling. The metaheuristic can be classified as a memetic algorithm since it is based on a population search in which periods of local optimization are interspersed with phases in which new configurations are created from earlier well-developed configurations or local minima of the previous iterative improvement process. The new population is created using crossover operators as in genetic algorithms. In this paper we discuss how a methodology inspired in Competitive Analysis may be relevant to the problem of designing better crossover operators. RESUMO: No ultimo ano uma nova metaheurstica para o problema de colora~c~ao em grafos foi apre-sentada por Costa, Hertz e Dubuis. Eles mostraram, com experimentos computacionais, algumas indica~c~oes claras dos benefcios desta nova tecnica. Colora~c~ao em grafos tem muitas aplica~c~oes, especialmente na area de programa~c~ao de tarefas, localiza~c~ao e horario . A metaheurstica pode ser classificada como algoritmo memetico desde que seja baseada em uma busca de popula~c~ao cujos perodos de otimiza~c~ao local s~ao intercalados com fases onde novas configura~c~oes s~ao criadas a partir de boas configura~c~oes ou mnimos locais de itera~c~oes anteriores. A nova popula~c~ao e criada usando opera~c~oes de crossover como em algoritmos geneticos. Neste artigo apresen-tamos como uma metodologia baseada em Competitive Analysis pode ser relevante para construir opera~c~oes de crossover. 
`.
Question: Which category should Paper #1128201 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