Optimizing Personal Computer Configurationswith Heuristic-Based Search Methods (original) (raw)

Abstract

Given the diversity and limitedcompatibility for personal computer hardware,obtaining an (sub-)optimal configuration fordifferent usage restricted to some budgetlimits and other possible criteria can bechallenging. In this paper, we firstlyformulated these common configuration problemsas discrete optimization problems to flexiblyadd in or modify users' requirements. Moreinterestingly, we proposed two intelligentoptimizers: a simple-yet-powerful beam searchmethod and a min-conflict heuristic-basedmicro-genetic algorithm (MGA) to solve thisreal-life optimization problem. Theheuristic-based MGA consistently outperformedthe beam search and branch-and-bound method inmost test cases. Furthermore, our work opens upexciting directions for investigation.

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References

• Aarts, E. & Korst, J. (1989). Boltzmann Machines for Traveling Salesman Problems. European Journal of Operational Research 39: 79–95.
  Google Scholar
• Cormen, T. H., Leiserson, C. E. & Rivest, R. L. (1994). Introduction to Algorithm. TheMIT Press.
• Firebaugh, M. W. (1988). Artificial Intelligence: A Knowledge-Based Approach. PWS-Kent Publishing Company, Boston.
  Google Scholar
• Minton, S., Johnston, M., Philips, A. & Laird, P. (1992). Minimizing Conflicts: A Heuristic Repair Method for Constraint Satisfaction and Scheduling Problem. Artificial Intelligence 58: 161–205.
  Google Scholar
• Rojas, M.C.R. (1996). From Quasi-Solution to Solution: An Evolutionary Algorithm to Solve CSP. In Proceedings of Principles and Practice of Constraint Programming (CP96), 367–381.
• Tam, V. & Stuckey, P. (1999). Improving Evolutionary Algorithms for Efficient Constraint Satisfaction. International Journal on Artificial Intelligence Tools 8 (World Scientific Publishers, December).
• Tam, V., Foo, W.K. & Gupta, R.K. (2000). A Fast and Flexible Framework of Scripting for Web Application Development: A Preliminary Experience Report. In Proceedings of 1st International Conference on Web Information Systems Engineering, 434–439.
• Thornton, A.C. (1994). Genetic Algorithms versus Simulated Annealing: Satisfaction of Large Sets of Algebraic Mechanical Design Constraints. In Proceedings of Artificial Intelligence in Design, 381–398.
• Tsang, E. (1993). Foundations of Constraint Satisfaction. Academic Press.

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Authors and Affiliations

1. Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong
   Vincent Tam
2. Department of Computer Science, School of Computing, National University of Singapore, Lower Kent Ridge Road, 119260, Singapore
   K.T. Ma

Authors

1. Vincent Tam
2. K.T. Ma

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Tam, V., Ma, K. Optimizing Personal Computer Configurationswith Heuristic-Based Search Methods.Artificial Intelligence Review 17, 129–140 (2002). https://doi.org/10.1023/A:1014587626020

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• Issue date: April 2002
• DOI: https://doi.org/10.1023/A:1014587626020