Above are papers related to the following paper: [Paper_ID] Paper #1131359. 	[Title] Comparison of the SAW-ing Evolutionary Algorithm and the Grouping Genetic Algorithm for Graph Coloring 1 [Abstract] 1 This report is also available through http://www.wi.leidenuniv.nl/~gusz/ sawvsgga.ps.gz 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 #646913. 	[Title] Graph Coloring with Adaptive Evolutionary Algorithms [Abstract] This paper presents the results of an experimental investigation on solving graph coloring problems with Evolutionary Algorithms (EA). After testing different algorithm variants we conclude that the best option is an asexual EA using order-based representation and an adaptation mechanism that periodically changes the fitness function during the evolution. This adaptive EA is general, using no domain specific knowledge, except, of course, from the decoder (fitness function). We compare this adaptive EA to a powerful traditional graph coloring technique DSatur and the Grouping GA on a wide range of problem instances with different size, topology and edge density. The results show that the adaptive EA is superior to the Grouping GA and outperforms DSatur on the hardest problem instances. Furthermore, it scales up better with the problem size than the other two algorithms and indicates a linear computational complexity. 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 #646900. 	[Title] Evaluating and Improving Steady State Evolutionary Algorithms on Constraint Satisfaction Problems [Abstract]  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 #646809. 	[Title] Orgy in the Computer: Multi-Parent Reproduction in Genetic Algorithms [Abstract] In this paper we investigate the phenomenon of multi-parent reproduction, i.e. we study recombination mechanisms where an arbitrary n &gt; 1 number of parents participate in creating children. In particular, we discuss scanning crossover that generalizes the standard uniform crossover and diagonal crossover that generalizes 1-point crossover, and study the effects of different number of parents on the GA behavior. We conduct experiments on tough function optimization problems and observe that by multi-parent operators the performance of GAs can be enhanced significantly. We also give a theoretical foundation by showing how these operators work on distributions. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1132731. 	[Title] Go and Genetic Programming Playing Go with Filter Functions [Abstract]  Please summarise all the given information.

Paper #1131359 has the title `Comparison of the SAW-ing Evolutionary Algorithm and the Grouping Genetic Algorithm for Graph Coloring 1  
` and the abstract `1 This report is also available through http://www.wi.leidenuniv.nl/~gusz/ sawvsgga.ps.gz 
`.
Question: Which category should Paper #1131359 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #1131359 has the title `Comparison of the SAW-ing Evolutionary Algorithm and the Grouping Genetic Algorithm for Graph Coloring 1  
` and the abstract `1 This report is also available through http://www.wi.leidenuniv.nl/~gusz/ sawvsgga.ps.gz 
`.
Question: Which category should Paper #1131359 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