Simulation Of Time-Continuous Chaotic UEDA Oscillator As The Generator Of Random Numbers For Heuristic

Abstract

This paper investigates the utilization of the timecontinuous chaotic system, which is UEDA oscillator, as the chaotic pseudo random number generator. (CPRNG). Repeated simulations were performed investigating the influence of the oscillator sampling time to the selected heuristic, which is differential evolution algorithm (DE). Initial experiments were performed on the selected test function in higher dimensions.

Influence of (P)rngs Onto Gpa-Es Behaviors

“…Because it is need to use large number of seed magnitudes for each function and large populations in each experiment, there it is extreme computational power need and experiments are calculated on supercomputer. Fortunately, if structure of the algorithm is known, it is possible to estimate probability of occurrence of given structure in the population and transpose results from measured experiments to another test case, as it was presented in [6,13,18], where the first monograph presents many ways based especially on the schema theory, the second paper brings approach close to Markov processes.…”

“…On the opposite side, there exist signals that on the place of random number generator it is possible to apply not only (pseudo) Random Number Generators [9], but also deterministic chaos systems and even deterministic functions as sin(x) without significant loss of evolution algorithm abilities. Such observations [1,2,7,8,[15][16][17][18] opened question which property of function applied on the place of random number generator are significant, if these property significance does not vary during evolutionary process and in different evolutionary operators implemented by used evolutionary algorithm. It also means that there might be used different generator for initial population formation, parameter identification (in the case of GPA-ES algorithm [12]), particular evolutionary operators control etc.…”

Simulation Of Chaotic Dynamics For Chaos Based Optimization – An Extended Study

“…The Figure 2 show the example of dynamical sequencing during the generating of pseudo number numbers transferred into the range <0 -1> by means of particular studied CPRNGs and with the sampling rate of 0.5s. The dependency of sequencing and periodicity on the sampling rate is discussed in details in (Senkerik et al 2015)…”