Name
Abstract | ||
|---|---|---|
Evolutionary methods and stochastic algorithms in general rely heavily on streams of (pseudo-)random numbers generated in course of their execution. The pseudo-random numbers are utilized for in-silico emulation of probability-driven natural processes such as modification of genetic information (mutation, crossover), partner selection, and survival of the fittest (selection, migration). Deterministic chaos is a very well known mathematical concept that can be used to generate sequences of real numbers within selected interval. In the past, it has been used as a basis for various pseudo-random number generators with interesting properties. This work provides an empirical comparison of the performance of genetic algorithms and differential evolution using different pseudo-random number generators and chaotic systems as sources of stochasticity. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/INCoS.2013.36 | INCoS |
Keywords | Field | DocType |
genetic algorithms,different pseudo-random number generator,differential evolution,genetic information,real number,genetic algorithm,evolutionary method,random number,deterministic chaos,various pseudo-random number generator,pseudo-random number,partner selection,random number generation | Crossover,Computer science,Evolutionary computation,Algorithm,Theoretical computer science,Genetic representation,Deterministic system,Random number generation,Quality control and genetic algorithms,Genetic algorithm,Randomness | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Krömer Pavel | 1 | 330 | 59.99 |
| Václav Snáel | 2 | 37 | 10.63 |
| Ivan Zelinka | 3 | 451 | 82.16 |