Combining Multiple Optimization Runs with Optimal Dependency Trees

Abstract

When trying to solve a combinatorial optimization problem, often multiple algorithms and/or multiple runs of the same algorithm are used in order to find multiple local minima. The information gained from previous search runs is commonly discarded when selecting initialization points for future runs. We present a method which uses information from previous runs to determine promising starting points for future searches. Our algorithm, termed COMIT, models inter-parameter dependencies present in the previously found high-evaluation solutions. COMIT incrementally learns optimal dependency trees that model the pairwise dependencies in a set of good solutions found in previous searches. COMIT then samples the probability distributions modeled by these trees to generate new starting points for future searches. This algorithm has been successfully applied to jobshop scheduling, traveling salesman, knapsack, rectangle packing, and bin-packing problems.

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Document Details

Document Type
Technical Report
Publication Date
Jun 30, 1997
Accession Number
ADA329937

Entities

People

  • Scott Davies
  • Shumeet Baluja

Organizations

  • Carnegie Mellon University

Tags

Communities of Interest

  • Autonomy
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Bayesian Networks
  • Coding
  • Computer Science
  • Data Sets
  • Genetic Algorithms
  • Homosexuality
  • Information Processing
  • Information Systems
  • Job Shop Scheduling
  • Machine Learning
  • Models
  • Optimization
  • Probabilistic Models
  • Probability
  • Probability Distributions
  • Scheduling (Production)

Fields of Study

  • Computer science

Readers

  • Graph Algorithms and Convex Optimization.
  • Operations Research