Results 1  10
of
10
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 ..."
Abstract

Cited by 92 (8 self)
 Add to MetaCart
(Show Context)
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 ..."
Abstract

Cited by 65 (3 self)
 Add to MetaCart
(Show Context)
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 ..."
Abstract

Cited by 59 (0 self)
 Add to MetaCart
(Show Context)
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.
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 ..."
Abstract

Cited by 23 (1 self)
 Add to MetaCart
(Show Context)
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...
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 ..."
Abstract

Cited by 21 (1 self)
 Add to MetaCart
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...
Essays of Discrete Structures: Purposeful Design of Grammatical Structures by Directed Stochastic Search
 Carnegie Mellon University
, 1997
"... This work presents a computational approach to the layout of discrete structures that incorporate practical design goals for routine and challenging design problems. The number of alternatives for the configuration of discrete structures that satisfy multiple design goals is quite large and the comp ..."
Abstract

Cited by 3 (1 self)
 Add to MetaCart
(Show Context)
This work presents a computational approach to the layout of discrete structures that incorporate practical design goals for routine and challenging design problems. The number of alternatives for the configuration of discrete structures that satisfy multiple design goals is quite large and the competition among design goals can make the relation between form and function unclear. Therefore, the objective of this work is a computational method capable of searching this illdefined design space to generate innovative design alternatives that enhance creativity and provide insight into formfunction relations for multiobjective structural design. A grammatical approach to structural design is enabled by applying shape annealing, a computational design technique that combines a grammatical formalism (shape grammars) with directed stochastic search (simulated annealing), to the layout of discrete structures. A shape grammar is used to define a language of discrete structures through the specification of spatial design transformations that implicitly represent the relation between form and function in trusses. Two shape grammars, a planar truss grammar and a singlelayer space truss grammar, will be presented. In order to generate purposeful designs from this language, an optimization model is
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 ..."
Abstract

Cited by 2 (0 self)
 Add to MetaCart
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 ...
TRANSISTOR LEVEL MICRO PLACEMENT AND ROUTING FOR TWODIMENSIONAL DIGITAL VLSI CELL Synthesis
, 2001
"... The automated synthesis of mask geometry for VLSI leaf cells, referred to as the cell synthesis problem, is an important component of any structured custom integrated circuit design environment. Traditional approaches based on the classic functional cell style of Uehara & VanCleemput pose this ..."
Abstract

Cited by 1 (0 self)
 Add to MetaCart
(Show Context)
The automated synthesis of mask geometry for VLSI leaf cells, referred to as the cell synthesis problem, is an important component of any structured custom integrated circuit design environment. Traditional approaches based on the classic functional cell style of Uehara & VanCleemput pose this problem as a straightforward onedimensional graph optimization problem for which optimal solution methods are known. However, these approaches are only directly applicable to static CMOS circuits and they break down when faced with more exotic logic styles. There is an increasing need in modern VLSI designs for circuits implemented in highperformance logic families such as Cascode Voltage Switch Logic (CVSL), Pass Transistor Logic (PTL), and domino CMOS. Circuits implemented in these nondual ratioed logic families can be highly irregular with complex geometry sharing and nontrivial routing. Such cells require a relatively unconstrained twodimensional fullcustom layout style which current methods are unable to synthesize. In this work we define the synthesis of complex twodimensional digital cells as a new problem which we call transistorlevel microplacement and routing. To address this problem we develop a complete endtoend methodology which is implemented in a prototype tool named TEMPO. A series of experiments on a new set of benchmark circuits verifies the effectiveness of
To appear in AISTATS97 Using Prediction to Improve Combinatorial Optimization Search
"... 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 ..."
Abstract
 Add to MetaCart
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
4. TITLE AND SUBTITLE
, 2000
"... Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments ..."
Abstract
 Add to MetaCart
(Show Context)
Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information,