no code implementations • 17 Sep 2020 • Roy Eliya, J. Michael Herrmann
We propose a new method for training an agent via an evolutionary strategy (ES), in which we iteratively improve a set of samples to imitate: Starting with a random set, in every iteration we replace a subset of the samples with samples from the best trajectories discovered so far.
no code implementations • 27 Nov 2018 • Ben Moews, J. Michael Herrmann, Gbenga Ibikunle
Trend change prediction in complex systems with a large number of noisy time series is a problem with many applications for real-world phenomena, with stock markets as a notoriously difficult to predict example of such systems.
no code implementations • 6 Sep 2017 • J. Michael Herrmann
Here, we provide evidence that the VM is written in natural language by establishing a relation of the Voynich alphabet and the Iranian Pahlavi script.
no code implementations • 19 Nov 2015 • J. Michael Herrmann, Adam Erskine, Thomas Joyce
Particle swarm optimisation is a metaheuristic algorithm which finds reasonable solutions in a wide range of applied problems if suitable parameters are used.
no code implementations • 27 Feb 2014 • Adam Erskine, J. Michael Herrmann
Particle Swarm Optimisation (PSO) makes use of a dynamical system for solving a search task.