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The biological mechanisms of evolution are well known: mutation, genes, selection, and so forth. You are only reading this because your ancestors were pretty good at surviving, and therefore had offspring. In fact, you owe your brain to evolution. What is not so well known is that evolution can be simulated on computers, and important problems can be solved(More)
The starting point for the analysis and experiments presented in this paper is a simplified elevator control problem, called 'S-ring'. As in many other real-world optimization problems, the exact fitness function evaluation is disturbed by noise. Evolution Strategies (ES) can generally cope with noisy fitness function values. It has been proposed that the(More)
The optimization of full-scale biogas plant operation is of great importance to make biomass a competitive source of renewable energy. The implementation of innovative control and optimization algorithms, such as Nonlinear Model Predictive Control, requires an online estimation of operating states of biogas plants. This state estimation allows for optimal(More)
The results obtained from the application of a genetic algorithm to the NP-complete maximum independent set problem are reported in this work. In contrast to many other genetic algorithm-based approaches that use domain-specific knowledge, the approach presented here relies on a graded penalty term component of the objective function to penalize infeasible(More)
Shell structure and magic numbers in atomic nuclei were generally explained by pioneering work that introduced a strong spin-orbit interaction to the nuclear shell model potential. However, knowledge of nuclear forces and the mechanisms governing the structure of nuclei, in particular far from stability, is still incomplete. In nuclei with equal neutron and(More)
Binary vectors of fixed length, the standard representation of solutions within canonical genetic algorithms, are discussed in this section, with some emphasis on the question of whether this representation should also be used for problems where the search space fundamentally differs from the space of binary vectors. Focusing on the example of continuous(More)
This tutorial gives a basic introduction to evolution strategies, a class of evolutionary algorithms. Key features such as mutation, recombination and selection operators are explained, and specifically the concept of self-adaptation of strategy parameters is introduced. All algorithmic concepts are explained to a level of detail such that an implementation(More)