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A method for determining the orbital parameters of interacting pairs of galaxies is presented and evaluated using artificial data. The method consists of a genetic algorithm which can search efficiently through the very large space of possible orbits. It is found that, in most cases, orbital parameters close to the actual orbital parameters of the pair can be found. The method does not require information about the velocity field of the interacting system, and is able to cope with noisy data. The inner regions of the galaxies, which are difficult to model, can be neglected, and the orbital parameters can be determined using the remaining information.
Modeling of interacting galaxies suffers from an extended parameter space prohibiting traditional grid based search strategies. As an alternative approach a combination of a Genetic Algorithm (GA) with fast restricted N-body simulations can be applie
Context: The detection and identification of oscillation modes (in terms of their $ell$, $m$ and successive $n$) is a great challenge for present and future asteroseismic space missions. The peak tagging is an important step in the analysis of these
A population of committees of agents that learn by using neural networks is implemented to simulate the stock market. Each committee of agents, which is regarded as a player in a game, is optimised by continually adapting the architecture of the agen
The paper presents a solution for the problem of choosing a method for analytical determining of weight factors for a genetic algorithm additive fitness function. This algorithm is the basis for an evolutionary process, which forms a stable and effec
Type IIP supernovae are recognized as independent extragalactic distance indicators, however, keeping in view of the diverse nature of their observed properties as well as the availability of good quality data, more and newer events need to be tested