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Randomized Experiments01:13

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The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
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Biased Random-Key Genetic Algorithms for the Winner Determination Problem in Combinatorial Auctions.

Carlos Eduardo de Andrade1, Rodrigo Franco Toso2, Mauricio G C Resende3

  • 1Institute of Computing, University of Campinas, Avenida Albert Einstein 1251, Campinas, SP 13083-852, Brazil andrade@ic.unicamp.br / ce.andrade@gmail.com.

Evolutionary Computation
|October 10, 2014
PubMed
Summary

This study introduces novel genetic algorithms for combinatorial auctions to maximize profit. These algorithms show competitive results, especially for large-scale auctions, improving upon existing methods.

Keywords:
Combinatorial auctionsbiased random-key genetic algorithmsgenetic algorithmswinner determination problem

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Area of Science:

  • Operations Research
  • Artificial Intelligence
  • Computational Economics

Background:

  • Combinatorial auctions are complex marketplaces requiring efficient winner determination.
  • Maximizing profit in these auctions, especially under a first-price model, is a significant challenge.
  • Existing methods often struggle with large-scale auction instances.

Purpose of the Study:

  • To develop and evaluate new algorithms for the winner determination problem in combinatorial auctions.
  • To enhance profit maximization strategies using a first-price model.
  • To assess the performance of novel genetic algorithms against established methods.

Main Methods:

  • Introduction of six variants of biased random-key genetic algorithms.
  • Development of a novel initialization technique using linear programming relaxations for genetic algorithm chromosomes.
  • Experimental comparison with mixed integer linear programming, specialized exact algorithms, and existing heuristics.

Main Results:

  • The proposed genetic algorithms demonstrate competitive performance.
  • Strong results were achieved, particularly for large-scale combinatorial auctions.
  • The novel initialization technique shows promise in enhancing algorithm effectiveness.

Conclusions:

  • Biased random-key genetic algorithms offer a viable and effective approach to combinatorial auction winner determination.
  • These algorithms provide a valuable alternative for optimizing profit in large-scale auctions.
  • Further research can explore hybrid approaches combining genetic algorithms with other optimization techniques.