Above are papers related to the following paper: [Paper_ID] Paper #307656. 	[Title] Plateaus and Plateau Search in Boolean Satisfiability Problems: When to Give Up Searching and Start Again [Abstract] We empirically investigate the properties of the search space and the behavior of hill-climbing search for solving hard, random Boolean satisfiability problems. In these experiments it was frequently observed that rather than attempting to escape from plateaus by extensive search, it was better to completely restart from a new random initial state. The optimum point to terminate search and restart was determined empirically over a range of problem sizes and complexities. The growth rate of the optimum cutoff is faster than linear with the number of features, although the exact growth rate was not determined. Based on these empirical results, a simple run-time heuristic is proposed to determine when to give up searching a plateau and restart. This heuristic closely approximates the empirically determined optimum values over a range of problem sizes and complexities, and consequently allows the search algorithm to automatically adjust its strategy for each particular problem without prior knowledge of the problem's complexity. 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 #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 #270085. 	[Title] When Gravity Fails: Local Search Topology [Abstract] Local search algorithms for combinatorial search problems frequently encounter a sequence of states in which it is impossible to improve the value of the objective function; moves through these regions, called plateau moves, dominate the time spent in local search. We analyze and characterize plateaus for three different classes of randomly generated Boolean Satisfiability problems. We identify several interesting features of plateaus that impact the performance of local search algorithms. We show that local minima tend to be small but occasionally may be very large. We also show that local minima can be escaped without unsatisfying a large number of clauses, but that systematically searching for an escape route may be computationally expensive if the local minimum is large. We show that plateaus with exits, called benches, tend to be much larger than minima, and that some benches have very few exit states which local search can use to escape. We show that the solutions (i.e., global minima) of randomly generated problem instances form clusters, which behave similarly to local minima. We revisit several enhancements of local search algorithms and explain their performance in light of our results. Finally we discuss strategies for creating the next generation of local search algorithms. 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 #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.

Paper #307656 has the title `Plateaus and Plateau Search in Boolean Satisfiability Problems: When to Give Up Searching and Start Again  
` and the abstract `We empirically investigate the properties of the search space and the behavior of hill-climbing search for solving hard, random Boolean satisfiability problems. In these experiments it was frequently observed that rather than attempting to escape from plateaus by extensive search, it was better to completely restart from a new random initial state. The optimum point to terminate search and restart was determined empirically over a range of problem sizes and complexities. The growth rate of the optimum cutoff is faster than linear with the number of features, although the exact growth rate was not determined. Based on these empirical results, a simple run-time heuristic is proposed to determine when to give up searching a plateau and restart. This heuristic closely approximates the empirically determined optimum values over a range of problem sizes and complexities, and consequently allows the search algorithm to automatically adjust its strategy for each particular problem without prior knowledge of the problem's complexity. 
`.
Question: Which category should Paper #307656 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #307656 has the title `Plateaus and Plateau Search in Boolean Satisfiability Problems: When to Give Up Searching and Start Again  
` and the abstract `We empirically investigate the properties of the search space and the behavior of hill-climbing search for solving hard, random Boolean satisfiability problems. In these experiments it was frequently observed that rather than attempting to escape from plateaus by extensive search, it was better to completely restart from a new random initial state. The optimum point to terminate search and restart was determined empirically over a range of problem sizes and complexities. The growth rate of the optimum cutoff is faster than linear with the number of features, although the exact growth rate was not determined. Based on these empirical results, a simple run-time heuristic is proposed to determine when to give up searching a plateau and restart. This heuristic closely approximates the empirically determined optimum values over a range of problem sizes and complexities, and consequently allows the search algorithm to automatically adjust its strategy for each particular problem without prior knowledge of the problem's complexity. 
`.
Question: Which category should Paper #307656 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