➤ Control engineering and simulation technology
➤ Control engineering 2
➤ Digital and event-driven control
Master`s programme➤ Control theory - nonlinear control ➤ Nature-inspired problem solving and machine learning ➤ Stochastic and model-predictive control
➤ Analytic tools and control design of nonlinear systems
➤ Procedures of metaheuristic: bio-inspired problem solution, evolutionary algorithm and game theory
➤ Member of extended senate HTWK Leipzig
➤ Member of audit committee Faculty of Engineering
➤ Member of faculty board
➤ EvoMusArt 2018, Parma, Italy: Best paper award nominee. Visual art inspired by the collective feeding behavior of sand-bubbler crabs.
➤ IEEE WCCI 2016, Vancouver, Canada: Regular best paper award. Analyzing coevolutionary games with dynamic fitness landscapes.
➤ EvoComplex 2012, Malaga, Spain: Best paper award nominee. Analyzing dynamic fitness landscapes of the targeting problem of chaotic systems.
We study creating and breaking symmetry in digitally gen-erated artificial-life-based visual art. Therefore, an artificial swarm-based pattern-making system is used as a test bed. The patterns are generated algorithmically by emulating the collective feeding behavior of sand-bubbler crabs. Our fo-cus is on analyzing concepts and templates for incorporating symmetry and broken symmetry into the creation process of bioinspired art. All four types of two-dimensional symmetry defined by isometric maps are used to create images. Apart from treating geometric symmetry, we also consider color as an object of symmetric transformations. Color symmetry is realized as a color permutation consistent with the isometric maps. Therefore, color permutation groups have been de-signed which utilize mappings on a color wheel.
Fitness landscapes are an abstract way for describing the relationship between the genetically possible (genotype), the actually realized (phenotype) and the survival/reproduction success (fitness). Differences in the fitness over genotypic space together with the Darwinian imperative to move into the direction of increasing fitness (codified by the notion of natural and sexual selection) results into the driving forces that are behind evolutionary processes. In other words, fitness landscapes are about analysing and visualizing the relationships between genotypes, phenotypes and fitness, while these relationships lay at the centre of attempts to mathematically describe evolutionary processes and evolutionary dynamics.
Adopting the basic idea from theoretical biology, fitness landscapes became increasingly popular in the 80ies of last century as a computational device for experimentally studying evolutionary scenario. Particularly stimulated through the works of Stuart Kauffman, Edward Weinberger and Terry Jones, amongst others, fitness landscape were also employed to analyse evolutionary search algorithms. For a certain time it was hoped that fitness landscape analysis would open up a way for predicting the performance of evolutionary algorithms for a given problem. Meanwhile, it has become clear that such an easily understandable relationship most likely cannot to be established, but landscape measures may be useful to establish different classes of problem hardness.
In the last couple of years, experimental and theoretical findings about the information transfer in biological systems have challenged the traditional understanding about the relationship between genotype, phenotype and fitness. Similarly, in evolutionary computation new topics in fitness landscape analysis emerged, for instance landscape measures and implications on problem hardness, visualizing concepts for fitness landscapes, coupled and deformable landscapes connected to coevolutionary phenomena or dynamic, stochastic and time-dependent fitness landscapes
The tutorial will give an introduction to the topic and address these recent developments in the theory and application of fitness landscapes and particularly cover the following topics.
- Principles and perspectives of fitness landscapes
- Examples of empirical and computational landscapes
- Topology, measures and problem hardness
- Coevolutionary and dynamic fitness landscapes
- Open questions and research topics
Studying evolutionary and coevolutionary games can be seen as an attempt to address a long-standing and fundamental problem in Darwinian evolution. How can the two seemingly contradictory observations be reconciled that individuals experience selective pressure which entails competition in order to be successful in survival and reproduction, but at the same time there is wide-spread cooperative and even altruistic behavior between individuals (and also groups of individuals or even species)? In other words, how can we have selection that favors fitter individuals over less fit, while the same individuals regularly cooperate with and support each other, thus ostensibly leveling off differences in fitness?
Co-evolutionary games may offer answers to these questions as they set up mathematical models to discuss whether, when and under what circumstances cooperation may be more advantageous than competition. Such games define individuals having behavioral choices to be players selecting and executing strategies (for instance cooperation or competition). By linking the relative costs and benefits of strategies to payoff (and subsequently fitness), we obtain a measure of how profitable a given choice is in evolutionary terms. Co-evolutionary games become dynamic if they are played iteratively over several rounds and the players may update their strategies and/or their networks of interaction, which describe with whom a given player interacts in the game. In this context it is common to call games where the players update exclusively their strategies evolutionary games, while games where the players may additionally update their interaction networks are called coevolutionary games.
The tutorial gives an overview about concepts and research questions as well as addressing recent developments in studying co-evolutionary games. Particularly it covers the following topics.
Concepts and mathematical models of evolutionary and coevolutionary games
- Players and strategies o Evolutionary graph theory
- Nash equilibria and evolutionary stable strategies (ESS)
- Strategies and configurations
Games and co-evolutionary dynamics
- Parameterization of the payoff matrix
- Social dilemma games: prisoner's dilemma, snowdrift, stag-hunt, harmony games
- Dilemma strength and universal scaling
- Frequency dependence
- Fixation properties: fixation probabilities and fixation times
- Computational issues
- Replicator dynamics
- Models for updating strategies and interaction networks
- Landscape view on coevolutionary games
- Open questions and research topics