Name
Title | ||
|---|---|---|
EA models and population fixed-points versus mutation rates for functions of unitation |
Abstract | ||
|---|---|---|
Using a dynamic systems model for the Simple Genetic Algorithm due to Vose[1], we analyze the fixed point behavior of the model without crossover applied to functions of unitation. Unitation functions are simplified fitness functions that reduce the search space into a smaller number of equivalence classes. This reduction allows easier computation of fixed points. We also create a dynamic systems model from a simple nondecreasing EA like the (1+1) EA and variants, then analyze this models on unitation classes. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1145/1068009.1068211 | GECCO |
Keywords | Field | DocType |
ea model,fitness function,unitation class,dynamic systems model,fixed point,easier computation,population fixed-points,mutation rate,equivalence class,simple genetic algorithm,fixed point behavior,unitation function,simple nondecreasing ea,evolutionary algorithm,dynamic system,search space,artificial intelligent,genetic algorithms,fixed points,population model,artificial intelligence,genetic algorithm | Population,Mathematical optimization,Crossover,Mutation rate,Computer science,Artificial intelligence,Fixed point,Equivalence class,Machine learning,Dynamical system,Genetic algorithm,Computation | Conference |
ISBN | Citations | PageRank |
1-59593-010-8 | 3 | 0.40 |
References | Authors | |
5 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| J. Neal Richter | 1 | 25 | 4.38 |
| John Paxton | 2 | 16 | 4.26 |
| Alden Wright | 3 | 24 | 2.69 |