Automatic Design of Ant Algorithms with Grammatical Evolution

Automatic Design of Ant Algorithms with Grammatical Evolution

Talk given at EuroGP in Málaga, Spain, April 13, 2012. The paper received the "Best Paper Award".


Jorge Tavares

April 17, 2012


  1. Automatic Design of Ant Algorithms with Grammatical Evolution Jorge Tavares

    Francisco B. Pereira CISUC, University of Coimbra, Portugal
  2. how do we design an Ant algorithm?

  3. “A man provided with paper, pencil, and rubber, and subject

    to strict discipline, is in effect a universal machine.” Alan Turing
  4. how can we automatically design an Ant algorithm?

  5. Grammatical Evolution + Ant Structures = Evolutionary Ant Algorithms

  6. running the system Initial Population Access Fitness of Population Satisfactory

    Individual exist ? Select Individuals Make Random Changes Return Best Individual Ant Algorithms Run Ant Algorithm to solve a problem Best Ant Algorithm for the problem
  7. <aco> ::= (aco <parameters-init> <optimization-cycle>) <optimization-cycle> ::= (repeat-until <loop-ants> <update-trails>

    <daemon-actions>) <loop-ants> ::= (foreach-ant make-solution-with <decision-policy> (if <bool> (local-update-trails <decay>))) <decision-policy> ::= (roulette-selection) | (q-selection <q-value>) | (random-selection) <update-trails> ::= (progn <evaporate> <reinforce>) ... ...
  8. the system is able to generate all main Ant algorithms

    Ant System (AS), Elitist Ant System (EAS), Rank- Based Ant System (RAS), Ant Colony System (ACS), Max-Min Ant System (MMAS)
  9. the search space contains many other combinations that define alternative

    Ant algorithms
  10. does search converge to manually designed Ant algorithms?

  11. can we find novel Ant algorithms?

  12. test problem: traveling salesman problem selected instances from TSPLIB: learning:

    eil76, pr76, gr96 testing: att48, eil51, berlin52, kroA100, lin105, gr137, u159, d198, pr226, lin318
  13. 0! 0.05! 0.1! 0.15! 0.2! 0.25! 0.3! 0.35! 1! 5!

    9! 13! 17! 21! 25! Distance to optimum! Generations! median of best individuals
  14. evolution summary (30 runs)

  15. search does not converge to manually designed architectures

  16. main components frequency

  17. evolved example: ei7618

  18. but... are the evolved Ant algorithms effective?

  19. optimization results

  20. statistical analysis

  21. comparison with standard algorithms

  22. statistical analysis

  23. hybrid between the two best evolved

  24. 1) evolution is able to discover original architectures 2) best

    evolved strategies exhibit a good generalization capability 3) competitive with human-designed variants 4) hybrid architecture based on evolved strategies is effective conclusions
  25. “We can only see a short distance ahead, but we

    can see plenty there that needs to be done.” Alan Turing
  26. Thank you. Questions? Special thanks to Alex Wild for all

    the Ant pictures