Genetic Algorithms

Genetic Algorithms (GAs) are a search technique inspired by Genetics and Evolution. GAs have been used extensively in order to solve a wide range of optimization problems. An excellent description of GAs can be found here or here. A presentation on the application of GAs to a network design problem (ring design) can be found here. GAs have been combined with Simulated Annealing and Tabu Search in order to provide more powerful and flexible search algorithms. Useful references to GAs can be found by following these links and the links contained within them:

Publications

  1. An Introduction to Genetic Algorithms
  2. Evolutionary Algorithms in Engineering Applications
  3. Adaptation in Natural and Artificial Systems
  4. Emergent Computing Methods in Engineering Design
 Links to Evolutionary Computation (EC) material (includes GAs)
  1. General collection of links on GAs
  2. Illinois Genetic Algorithms Laboratory (IlliGAL)
  3. Carleton School of Computer Science links on Evolutionary Computation
  4. Nova Genetica by David Molnar
  5. The Genetic Algorithm Archive maintained by the US Navy
  6. EvolutioNary COmputationREpository network
  7. EVONET The European Network of Excellence in Evolutionary Computing
  8. Artificial Evolution Archive
Groups involved in Evolutionary Computation (includes GAs)
  1. University of Sheffield Evolutionary Computation Projects
  2. University of East Anglia Mathematical Algorithms Group (MAG)
  3. UMich Genetic Algorithm Research Group (MSU GARAGe)
  4. Evolutionary Computing Group at UWE, Bristol
  5. Plymouth Engineering Design Centre
  6. Evolutionary Computation Research Group at Napier University, Edinburgh
  7. Evolutionary and Adaptive Systems (EASy) at Sussex University
Demonstration applets
  1. Gamelan archive of Java applets
This page is in its infancy, and will ultimately contain many links to applications related to EC.