Crosshair Optimizer

Metaheuristic optimization algorithms are heuristics that are capable of creating a good enough\u27\u27 solution to a computationally complex problem. Algorithms in this area of study are focused on the process of exploration and exploitation: exploration of the solution space and exploitation of the results that have been found during that exploration, with most resources going toward the former half of the process. The novel Crosshair optimizer developed in this thesis seeks to take advantage of the latter, exploiting the best possible result as much as possible by directly searching the area around that best result with a stochastic approach. This research seeks to prove that the Crosshair Optimizer is comparable, if not better in some aspects, to current established metaheuristics optimization algorithms, not only in obtaining optimal results, but usability in high performance computing, and versatility through the use of multiple randomizers and parameter tuning

Hidden periodicity - chaos dependance on numerical precision

Deterministic chaos has been observed in many systems and seems to be random-like for external observer. Chaos, especially of discrete systems, has been used on numerous occasions in place of random number generators in so called evolutionary algorithms. When compared to random generators, chaotic systems generate values via so called map function that is deterministic and thus, the next value can be calculated, i.e. between elements of random series is no deterministic relation, while in the case of chaotic system it is. Despite this fact, the very often use of chaotic generators improves the performance of evolutionary algorithms. In this paper, we discuss the behavior of two selected chaotic system (logistic map and Lozi system) with dependance on numerical precision and show that numerical precision causes the appearance of many periodic orbits and explain reason why it is happens. © Springer International Publishing Switzerland 2013