Above are papers related to the following paper: [Paper_ID] Paper #58453. 	[Title] Intelligent Gradient-Based Search of Incompletely Defined Design Spaces [Abstract] Gradient-based numerical optimization of complex engineering designs offers the promise of rapidly producing better designs. However, such methods generally assume that the objective function and constraint functions are continuous, smooth, and defined everywhere. Unfortunately, realistic simulators tend to violate these assumptions. We present a rule-based technique for intelligently computing gradients in the presence of such pathologies in the simulators, and show how this gradient computation method can be used as part of a gradient-based numerical optimization system. We tested the resulting system in the domain of conceptual design of supersonic transport aircraft, and found that using rule-based gradients can decrease the cost of design space search by one or more orders of magnitude. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #58436. 	[Title] Intelligent Model Selection for Hillclimbing Search in Computer-Aided Design [Abstract] Models of physical systems can differ according to computational cost, accuracy and precision, among other things. Depending on the problem solving task at hand, different models will be appropriate. Several investigators have recently developed methods of automatically selecting among multiple models of physical systems. Our research is novel in that we are developing model selection techniques specifically suited to computer-aided de sign. Our approach is based on the idea that artifact performance models for computer-aided design should be chosen in light of the design decisions they are required to support. We have developed a technique called "Gradient Magnitude Model Selection" (GMMS), which embodies this principle. GMMS operates in the context of a hillclimbing search process. It selects the simplest model that meets the needs of the hillclimbing algorithm in which it operates. We are using the domain of sailing yacht design as a testbed for this research. We have implemented GMMS and used it in hillclimb-ing search to decide between a computationally expensive potential-flow program and an algebraic approximation to analyze the performance of sailing yachts. Experimental tests show that GMMS makes the design process faster than it would be if the most expensive model were used for all design evaluations. GMMS achieves this performance improvement with little or no sacrifice in the quality of the resulting design. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1128453. 	[Title] An Adaptive Penalty Approach for Constrained Genetic-Algorithm Optimization [Abstract] In this paper we describe a new adaptive penalty approach for handling constraints in genetic algorithm optimization problems. The idea is to start with a relatively small penalty coefficient and then increase it or decrease it on demand as the optimization progresses. Empirical results in several engineering design domains demonstrate the merit of the proposed approach. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #801170. 	[Title] Guided Crossover: A New Operator for Genetic Algorithm Based Optimization [Abstract] Genetic algorithms (GAs) have been extensively used in different domains as a means of doing global optimization in a simple yet reliable manner. They have a much better chance of getting to global optima than gradient based methods which usually converge to local sub optima. However, GAs have a tendency of getting only moderately close to the optima in a small number of iterations. To get very close to the optima, the GA needs a very large number of iterations. Whereas gradient based optimizers usually get very close to local optima in a relatively small number of iterations. In this paper we describe a new crossover operator which is designed to endow the GA with gradient-like abilities without actually computing any gradients and without sacrificing global optimality. The operator works by using guidance from all members of the GA population to select a direction for exploration. Empirical results in two engineering design domains and across both binary and floating point representations demonstrate that the operator can significantly improve the steady state error of the GA optimizer. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #289779. 	[Title] Adaptation of Genetic Algorithms for Engineering Design Optimization [Abstract] Genetic algorithms have been extensively used in different domains as a means of doing global optimization in a simple yet reliable manner. However, in some realistic engineering design optimization domains it was observed that a simple classical implementation of the GA based on binary encoding and bit mutation and crossover was sometimes inefficient and unable to reach the global optimum. Using floating point representation alone does not eliminate the problem. In this paper we describe a way of augmenting the GA with new operators and strategies that take advantage of the structure and properties of such engineering design domains. Empirical results (initially in the domain of conceptual design of supersonic transport aircraft and the domain of high performance supersonic missile inlet design) demonstrate that the newly formulated GA can be significantly better than the classical GA in terms of efficiency and reliability. http://www.cs.rutgers.edu/~shehata/papers.html Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #289780. 	[Title] Learning to be Selective in Genetic-Algorithm-Based Design Optimization [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #58454. 	[Title] Learning Prototype-Selection Rules for Case-Based Iterative Design seen as a case-based reasoning system [4], in [Abstract] The first step for most case-based design systems is to select an initial prototype from a database of previous designs. The retrieved prototype is then modified to tailor it to the given goals. For any particular design goal the selection of a starting point for the design process can have a dramatic effect both on the quality of the eventual design and on the overall design time. We present a technique for automatically constructing effective prototype-selection rules. Our technique applies a standard inductive-learning algorithm, C4.5, to a set of training data describing which particular prototype would have been the best choice for each goal encountered in a previous design session. We have tested our technique in the domain of racing-yacht-hull design, comparing our inductively learned selection rules to several competing prototype-selection methods. Our results show that the inductive prototype-selection method leads to better final designs when the design process is guided by a noisy evaluation function, and that the inductively learned rules will often be more efficient than competing methods. Many automated design systems begin by retrieving an initial prototype from a library of previous designs, using the given design goal as an index to guide the retrieval process [14]. The retrieved prototype is then modified by a set of design modification operators to tailor the selected design to the given goals. In many cases the quality of competing designs can be assessed using domain-specific evaluation functions, and in such cases the design-modification process is often This research has benefited from numerous discussions with members of the Rutgers CAP project. We thank Andrew Gelsey for helping with the cross-validation code, John Keane for helping with RUVPP, and Andrew Gelsey and Tim Weinrich for comments on a previous draft of this paper. This research was supported under ARPA-funded NASA grant NAG 2-645. In the context of such case-based design systems, the choice of an initial prototype can affect both the quality of the final design and the computational cost of obtaining that design, for three reasons. First, prototype selection may impact quality when the prototypes lie in disjoint search spaces. In particular, if the system's design modification operators cannot convert any prototype into any other prototype, the choice of initial prototype will restrict the set of possible designs that can be obtained by any search process. A poor choice of initial prototype may therefore lead to a suboptimal final design. Second, prototype selection may impact quality when the design process is guided by a nonlinear evaluation function with unknown global properties. Since there is no known method that is guaranteed to find the global optimum of an arbitrary nonlinear function [7], most design systems rely on iterative local search methods whose results are sensitive to the initial starting point. Finally, the choice of prototype may have an impact on the time needed to carry out the design modification process|two different starting points may yield the same final design but take very different amounts of time to get there. In design problems where evaluating even just a single design can take tremendous amounts of time, selecting an appropriate initial prototype can be the determining factor in the success or failure of the design process. This paper describes the application of inductive learning [11] to form rules for selecting appropriate prototype designs. The paper is structured as follows. In Section 2, we describe our inductive method for learning prototype-selection rules. In Section 3 we describe the domain of racing-yacht-hull design, in which we tested our prototype-selection methods. In Sections 4 and 5, we describe the experiments Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #289781. 	[Title] A Genetic Algorithm for Continuous Design Space Search [Abstract] Genetic algorithms (GAs) have been extensively used as a means for performing global optimization in a simple yet reliable manner. However, in some realistic engineering design optimization domains the simple, classical implementation of a GA based on binary encoding and bit mutation and crossover is often inefficient and unable to reach the global optimum. In this paper we describe a GA for continuous design-space optimization that uses new GA operators and strategies tailored to the structure and properties of engineering design domains. Empirical results in the domains of supersonic transport aircraft and supersonic missile inlets demonstrate that the newly formulated GA can be significantly better than the classical GA in both efficiency and reliability. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #87417. 	[Title] Using Modeling Knowledge to Guide Design Space Search [Abstract] Automated search of a space of candidate designs seems an attractive way to improve the traditional engineering design process. To make this approach work, however, the automated design system must include both knowledge of the modeling limitations of the method used to evaluate candidate designs and also an effective way to use this knowledge to influence the search process. We suggest that a productive approach is to include this knowledge by implementing a set of model constraint functions which measure how much each modeling assumptions is violated, and to influence the search by using the values of these model constraint functions as constraint inputs to a standard constrained nonlinear optimization numerical method. We test this idea in the domain of conceptual design of supersonic transport aircraft, and our experiments indicate that our model constraint communication strategy can decrease the cost of design space search by one or more orders of magnitude. Please summarise all the given information.

Paper #58453 has the title `Intelligent Gradient-Based Search of Incompletely Defined Design Spaces  
` and the abstract `Gradient-based numerical optimization of complex engineering designs offers the promise of rapidly producing better designs. However, such methods generally assume that the objective function and constraint functions are continuous, smooth, and defined everywhere. Unfortunately, realistic simulators tend to violate these assumptions. We present a rule-based technique for intelligently computing gradients in the presence of such pathologies in the simulators, and show how this gradient computation method can be used as part of a gradient-based numerical optimization system. We tested the resulting system in the domain of conceptual design of supersonic transport aircraft, and found that using rule-based gradients can decrease the cost of design space search by one or more orders of magnitude.
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Question: Which category should Paper #58453 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #58453 has the title `Intelligent Gradient-Based Search of Incompletely Defined Design Spaces  
` and the abstract `Gradient-based numerical optimization of complex engineering designs offers the promise of rapidly producing better designs. However, such methods generally assume that the objective function and constraint functions are continuous, smooth, and defined everywhere. Unfortunately, realistic simulators tend to violate these assumptions. We present a rule-based technique for intelligently computing gradients in the presence of such pathologies in the simulators, and show how this gradient computation method can be used as part of a gradient-based numerical optimization system. We tested the resulting system in the domain of conceptual design of supersonic transport aircraft, and found that using rule-based gradients can decrease the cost of design space search by one or more orders of magnitude.
`.
Question: Which category should Paper #58453 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