Sandpile evo star 2011
•Download as PPSX, PDF•
0 likes•486 views
This document summarizes a study on using a genetic algorithm with a mutation operator based on the sandpile model of self-organized criticality to solve dynamic optimization problems. The sandpile genetic algorithm (GGASM) uses a 2D lattice to represent the population, where grains are dropped on the lattice at a rate g. When slopes exceed a critical value, grains topple to neighboring cells in avalanches whose sizes follow a power law distribution. This distribution determines the bit-level mutation rates. Experiments tested GGASM on dynamic problems with varying severity, frequency of changes, base functions, and grain rates g. Results showed the mutation rate distribution adapts to problem dynamics and GGASM outperforms algorithms without sandpile
Recommended
More Related Content
Viewers also liked
Viewers also liked (6)
More from Carlos M. Fernandes
More from Carlos M. Fernandes (20)
Recently uploaded
Recently uploaded (20)
Sandpile evo star 2011
- 1. A Study on the Mutation Rates of a Genetic Algorithm Interacting with a Sandpile Carlos M. Fernandes1,2 Juan L.J. Laredo1 Antonio .M. Mora1 Juan Julián Merelo1 Agostinho C. Rosa2 1Department of Computer Architecture, University of Granada, Spain 2LaSEEB-ISR-IST, Technical Univ. of Lisbon (IST), Portugal Evo*2011 – Torino, Italy, April 2011 1
- 2. Summary Motivation: DynamicOptimizationProblems. Self-OrganizedCriticality (SOC) andtheSandpileModel. SandpileMutation GA (GGASM). Mutation Rates. ConclusionsandFutureWork. 2 Evo*2011 – Torino, Italy, April 2011
- 3. DynamicOptimizationProblems Non-stationary (or dynamic) fitness functions: Fitness function depends on time t 3 Evo*2011 – Torino, Italy, April 2011
- 4. DynamicOptimizationProblems 4 Solve the stationary problem. Characteristics of the changes: Severity Frequency Cyclic Predictability Evolutionary Algorithms: full convergence must be avoided Evo*2011 – Torino, Italy, April 2011
- 5. Tracking the Optimum (Royal Road) 5
- 6. Dynamic Optimization and EC Reaction to Changes Increase Mutation Rate (Hypermutation) Diversity Maintenance Maintain genetic diversity at a higher level Random Immigrants Genetic Algorithm (RIGA) 6 Evo*2011 – Torino, Italy, April 2011
- 7. Self-Organized Criticality (SOC) Genetic Algorithm with a Self-Organized Criticality Mutation Operator (Sandpile Mutation) 7 SOC is state of criticality formed by self-organization in a long transient period at the border of order and chaos.
- 9. “Sand” is dropped on top of 2D lattice, increasing the number of grains in the cell.
- 10. When the slope exceeds a critical value, the grains topple to the neighbouring cells - AvalanchePower-law relationship between the size of the avalanches and their frequency. Evo*2011 – Torino, Italy, April 2011
- 11. SOC inEvolutionaryComputation Krink et al. compute the sandpile offline, and then use the avalanche size as the mutation probabilities. Self-Organized Random Immigrants GA uses a SOC model to introduce random immigrants in the population Sandpile Mutation: works on-line at the bit level 9 Evo*2011 – Torino, Italy, April 2011
- 13. If h(x,y) = 4, topple
- 14. Maximization: mutates if rand (0,1.0) > (normalized) fitnessParents’ fitness Evo*2011 – Torino, Italy, April 2011
- 17. severity :ρЄ [0, 1]
- 18. ρ×lenght-> number of variables that are affected by changes
- 19. Trap Function, Onemax, Royal Road, Knapsack…
- 20. Compute the offline performance: best fitness averaged over the entire run.11 Evo*2011 – Torino, Italy, April 2011
- 21. Performance 12 GGASMvs SORIGA (statistical tests) Evo*2011 – Torino, Italy, April 2011
- 22. Experimental Setup Order-3 Trap Functions and Onemax Population Size n = 30 Chromosome lenghtl = 30 30x30 sandpile pc=1.0, 2-elitism; uniform crossover. Several g values Varying frequency (1/ε) andseverity(ρ) Compare thepopulationbeforeandafterggrains are dropped. 13
- 23. Varying the Severity 14 Order-3 trap function Evo*2011 – Torino, Italy, April 2011
- 24. Mutation Rate in each generation 15 Evo*2011 – Torino, Italy, April 2011
- 25. Varying the Frequency 16 600000 evalutations order-3 trap functions Evo*2011 – Torino, Italy, April 2011
- 26. Different base-functions 17 ρ = random Evo*2011 – Torino, Italy, April 2011
- 27. Varying the grain rate g 18 ε = 12000; ρ = random n= 30; l = 30 order-3 traps Evo*2011 – Torino, Italy, April 2011
- 28. Performance 19 ε = 1200 error = 0.71% error = 2.59%
- 29. Conclusions The distribution of mutation rate varies with severity and frequency. Different base-function may lead to different distributions. The grain rate affects the distribution. The algorithmic description and the topology impose a limit to the mutation rate. 20 Evo*2011 – Torino, Italy, April 2011
- 31. Study the distribution rate and the optimal grain rate values when the sandpile grows.
- 33. Sandpile Topology.21 3-trap, 60 bits, n=60 Evo*2011 – Torino, Italy, April 2011
- 34. Questions? 22