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Related Experiment Videos

Constructive genetic algorithm for clustering problems.

L A Lorena1, J C Furtado

  • 1LAC-Instituto Nacional de Pesquisas Espaciais, Av. dos Astronautas 1758 - Caixa Postal 515, 12201-970 São José dos Campos-SP, Brazil. lorena@lac.inpe.br

Evolutionary Computation
|August 28, 2001
PubMed
Summary
This summary is machine-generated.

The Constructive Genetic Algorithm (CGA) enhances optimization by directly evaluating building blocks (schemata) using a novel double fitness function. This approach improves genetic algorithm performance on graph clustering problems.

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Area of Science:

  • Computational Intelligence
  • Operations Research
  • Computer Science

Background:

  • Genetic algorithms (GAs) are effective for optimization.
  • Building block construction (schemata) positively influences GA behavior.
  • Schemata are typically evaluated indirectly.

Purpose of the Study:

  • Introduce the Constructive Genetic Algorithm (CGA) for direct schemata evaluation.
  • Model problems as bi-objective optimization problems using a double fitness function (fg-fitness).
  • Enhance GA behavior through direct schemata assessment and dynamic population management.

Main Methods:

  • Developed a Constructive Genetic Algorithm (CGA) with a dual fitness evaluation (fg-fitness).
  • Implemented an adaptive rejection threshold for population ranking and dynamic size adjustment.
  • Applied CGA to p-median and capacitated p-median graph clustering problems using binary representations and greedy heuristics.

Main Results:

  • The CGA successfully evaluated schemata and structures on a common basis.
  • The dynamic population and adaptive threshold contributed to effective evolution.
  • Demonstrated good performance on benchmark graph clustering instances.

Conclusions:

  • The Constructive Genetic Algorithm (CGA) offers a novel approach to genetic algorithms by enabling direct schemata evaluation.
  • The fg-fitness and adaptive threshold provide robust mechanisms for optimization.
  • CGA shows promise for solving complex graph clustering problems.