Differential Evolution for Multiobjective Portfolio Optimization

Financial portfolio optimization is a challenging problem. First, the problem is multiobjective (i.e.: minimize risk and maximize profit) and the objective functions are often multimodal and non smooth (e.g.: value at risk). Second, managers have often to face real-world constraints, which are typically non-linear. Hence, conventional optimization techniques, such as quadratic programming, cannot be used. Stochastic search heuristic can be an attractive alternative. In this paper, we propose a new multiobjective algorithm for portfolio optimization: DEMPO - Differential Evolution for Multiobjective Portfolio Optimization. The main advantage of this new algorithm is its generality, i.e., the ability to tackle a portfolio optimization task as it is, without simplifications. Our empirical results show the capability of our approach of obtaining highly accurate results in very reasonable runtime, in comparison with quadratic programming and another state-of-art search heuristic, the so-called NSGA II.Portfolio Optimization, Multiobjective, Real-world Constraints, Value at Risk, Expected Shortfall, Differential Evolution

Exploring the Performance of an Evolutionary Algorithm for Greenhouse Control

Evolutionary algorithms for optimization of dynamic problems have recently received increasing attention. Online control is a particularly interesting class of dynamic problems, because of the interactions between the controller and the controlled system. In this paper, we report experimental results on two aspects of the direct control strategy in relation to a crop-producing greenhouse. In the first set of experiments, we investigated how to balance the available computation time between population size and generations. The second experiments were on different control horizons, and showed the importance of this aspect for direct control. Finally, we discuss the results in the wider context of dynamic optimization

Spider webs inspiring soft robotics

In soft robotics, bio-inspiration ranges from hard- to software. Orb web spiders provide excellent examples for both. Adapted sensors on their legs may use morphological computing to fine-tune feedback loops that supervise the handling and accurate placement of silk threads. The spider's webs embody the decision rules of a complex behaviour that relies on navigation and piloting laid down in silk by behaviour charting inherited rules. Analytical studies of real spiders allow the modelling of path-finding construction rules optimized in evolutionary algorithms. We propose that deconstructing spiders and unravelling webs may lead to adaptable robots able to invent and construct complex novel structures using relatively simple rules of thumb

Self-Adaptive Operator Scheduling using the Religion-Based EA

The optimal choice of the variation operators mutation and crossover and their parameters can be decisive for the performance of evolutionary algorithms (EAs). Usually the type of the operators (such as Gaussian mutation) remains the same during the entire run and the probabilistic frequency of their application is determined by a constant parameter, such as a fixed mutation rate. However, recent studies have shown that the optimal usage of a variation operator changes during the EA run. In this study, we combined the idea of self-adaptive mutation operator scheduling with the Religion-Based EA (RBEA), which is an agent model with spatially structured and variable sized subpopulations (religions). In our new model (OSRBEA), we used a selection of different operators, such that each operator type was applied within one specific subpopulation only. Our results indicate that the optimal choice of operators is problem dependent, varies during the run, and can be handled by our self-adaptive OSRBEA approach. Operator scheduling could clearly improve the performance of the already very powerful RBEA and was superior compared to a classic and other advanced EA approaches