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Automated Synthesis of Analog Electrical Circuits by Means of Genetic Programming
, 1997
"... The design (synthesis) of analog electrical circuits starts with a highlevel statement of the circuit's desired behavior and requires creating a circuit that satisfies the specified design goals. Analog circuit synthesis entails the creation of both the topology and the sizing (numerical values) of ..."
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Cited by 64 (8 self)
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The design (synthesis) of analog electrical circuits starts with a highlevel statement of the circuit's desired behavior and requires creating a circuit that satisfies the specified design goals. Analog circuit synthesis entails the creation of both the topology and the sizing (numerical values) of all of the circuit's components. The difficulty of the problem of analog circuit synthesis is well known and there is no previously known general automated technique for synthesizing an analog circuit from a highlevel statement of the circuit's desired behavior. This paper presents a single uniform approach using genetic programming for the automatic synthesis of both the topology and sizing of a suite of eight different prototypical analog circuits, including a lowpass filter, a crossover (woofer and tweeter) filter, a source identification circuit, an amplifier, a computational circuit, a timeoptimal controller circuit, a temperaturesensing circuit, and a voltage reference circuit. The problemspecific information required for each of the eight problems is minimal and consists primarily of the number of inputs and outputs of the desired circuit, the types of available components, and a fitness measure that restates the highlevel
Learning Evaluation Functions for Global Optimization and Boolean Satisfiability
 In Proc. of 15th National Conf. on Artificial Intelligence (AAAI
, 1998
"... This paper describes STAGE, a learning approach to automatically improving search performance on optimization problems. STAGE learns an evaluation function which predicts the outcome of a local search algorithm, such as hillclimbing or WALKSAT, as a function of state features along its search ..."
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Cited by 59 (3 self)
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This paper describes STAGE, a learning approach to automatically improving search performance on optimization problems. STAGE learns an evaluation function which predicts the outcome of a local search algorithm, such as hillclimbing or WALKSAT, as a function of state features along its search trajectories. The learned evaluation function is used to bias future search trajectories toward better optima. We present positive results on six largescale optimization domains.
Learning Evaluation Functions to Improve Optimization by Local Search
 Journal of Machine Learning Research
, 2000
"... This paper describes algorithms that learn to improve search performance on largescale optimization tasks. The main algorithm, Stage, works by learning an evaluation function that predicts the outcome of a local search algorithm, such as hillclimbing or Walksat, from features of states visited durin ..."
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Cited by 56 (0 self)
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This paper describes algorithms that learn to improve search performance on largescale optimization tasks. The main algorithm, Stage, works by learning an evaluation function that predicts the outcome of a local search algorithm, such as hillclimbing or Walksat, from features of states visited during search. The learned evaluation function is then used to bias future search trajectories toward better optima on the same problem. Another algorithm, XStage, transfers previously learned evaluation functions to new, similar optimization problems. Empirical results are provided on seven largescale optimization domains: binpacking, channel routing, Bayesian network structurefinding, radiotherapy treatment planning, cartogram design, Boolean satisfiability, and Boggle board setup.
Using Prediction to Improve Combinatorial Optimization Search
 In Proc. of 6th Int'l Workshop on Artificial Intelligence and Statistics
, 1997
"... To appear in AISTATS97 This paper describes a statistical approach to improving the performance of stochastic search algorithms for optimization. Given a search algorithm A, we learn to predict the outcome of A as a function of state features along a search trajectory. Predictions are made by a fun ..."
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Cited by 20 (1 self)
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To appear in AISTATS97 This paper describes a statistical approach to improving the performance of stochastic search algorithms for optimization. Given a search algorithm A, we learn to predict the outcome of A as a function of state features along a search trajectory. Predictions are made by a function approximator such as global or locallyweighted polynomial regression; training data is collected by MonteCarlo simulation. Extrapolating from this data produces a new evaluation function which can bias future search trajectories toward better optima. Our implementation of this idea, STAGE, has produced very promising results on two largescale domains. 1 Introduction The problem of combinatorial optimization is simply stated: given a finite state space X and an objective function f : X ! !, find an optimal state x = argmin x2X f(x). Typically, X is huge, and finding an optimal x is intractable. However, there are many heuristic algorithms that attempt to exploit f 's structur...
Value Function Based Production Scheduling
 In International Conference on Machine Learning
, 1998
"... Production scheduling, the problem of sequentially configuring a factory to meet forecasted demands, is a critical problem throughout the manufacturing industry. The requirement of maintaining product inventories in the face of unpredictable demand and stochastic factory output makes standard schedu ..."
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Cited by 17 (1 self)
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Production scheduling, the problem of sequentially configuring a factory to meet forecasted demands, is a critical problem throughout the manufacturing industry. The requirement of maintaining product inventories in the face of unpredictable demand and stochastic factory output makes standard scheduling models, such as jobshop, inadequate. Currently applied algorithms, such as simulated annealing and constraint propagation, must employ adhoc methods such as frequent replanning to cope with uncertainty. In this paper, we describe a Markov Decision Process (MDP) formulation of production scheduling which captures stochasticity in both production and demands. The solution to this MDP is a value function which can be used to generate optimal scheduling decisions online. A simple example illustrates the theoretical superiority of this approach over replanningbased methods. We then describe an industrial application and two reinforcement learning methods for generating an approximate valu...
Learning Evaluation Functions
 CMU CS Thesis Proposal
, 1996
"... Evaluation functions are an essential component of practical search algorithms for optimization, planning and control. Examples of such algorithms include hillclimbing, simulated annealing, bestfirst search, A*, and alphabeta. In all of these, the evaluation functions are typically built manually ..."
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Cited by 2 (0 self)
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Evaluation functions are an essential component of practical search algorithms for optimization, planning and control. Examples of such algorithms include hillclimbing, simulated annealing, bestfirst search, A*, and alphabeta. In all of these, the evaluation functions are typically built manually by domain experts, and may require considerable tweaking to work well. I will investigate the thesis that statistical machine learning can be used to automatically generate highquality evaluation functions for practical combinatorial problems. The data for such learning is gathered by running trajectories through the search space. The learned evaluation function may be applied either to guide further exploration of the same space, or to improve performance in new problem spaces which share similar features. Two general families of learning algorithms apply here: reinforcement learning and metaoptimization. The reinforcement learning approach, dating back to Samuel's checkers player [ 1959 ...