Lars Willmes

Comparing neural networks and Kriging for fitness approximation in evolutionary optimization

Neural networks and Kriging method are compared for constructing fitness approximation models in evolutionary optimization algorithms. The two models are applied in an identical framework to the optimization of a number of well known test functions. In addition, two different ways of training the approximators are evaluated: in one setting the models are built off-line using data from previous optimization runs and in the other setting the models are built online from the data available from the current optimization.

NOVEL HEAD AND NECK CANCER SURVIVAL ANALYSIS APPROACH: RANDOM SURVIVAL FORESTS VERSUS COX PROPORTIONAL HAZARDS REGRESSION

Electronic patient files generate an enormous amount of medical data. These data can be used for research, such as prognostic modeling. Automatization of statistical prognostication processes allows automatic updating of models when new data is gathered. The increase of power behind an automated prognostic model makes its predictive capability more reliable. Cox proportional hazard regression is most frequently used in prognostication. Automatization of a Cox model is possible, but we expect the updating process to be time‐consuming. A possible solution lies in an alternative modeling technique called random survival forests (RSFs). RSF is easily automated and is known to handle the proportionality assumption coherently and automatically. Performance of RSF has not yet been tested on a large head and neck oncological dataset. This study investigates performance of head and neck overall survival of RSF models. Performances are compared to a Cox model as the “gold standard.” RSF might be an interesting alternative modeling approach for automatization when performances are similar.

Evolution strategies for laser pulse compression

This study describes first steps taken to bring evolutionaryoptimization technology from computer simulations to real world experimentationin physics laboratories. The approach taken considers a wellunderstood Laser Pulse Compression problem accessible both to simulationand laboratory experimentation as a test function for variants ofEvolution Strategies. The main focus lies on coping with the unavoidablenoise present in laboratory experimentation. Results from simulations arecompared to previous studies and to laboratory experiments.

Evaluating Multi-criteria Evolutionary Algorithms for Airfoil Optimisation

A new approach for multi criteria design optimisation is presented in the paper. The problem tackled with this approach is the 2- dimensional design of an aircraft wing. To carry the derandomized step size control also to the multi criteria applications, four different selection schemes are proposed. Furthermore, we present a new method for averaging results of multi objective evolutionary algorithms. This method is then used to compare the results achieved with the proposed algorithms.

Inverse design of cellular automata by genetic algorithms: an unconventional programming paradigm

Evolving solutions rather than computing them certainly represents an unconventional programming approach. The general methodology of evolutionary computation has already been known in computer science since more than 40 years, but their utilization to program other algorithms is a more recent invention. In this paper, we outline the approach by giving an example where evolutionary algorithms serve to program cellular automata by designing rules for their iteration. Three different goals of the cellular automata designed by the evolutionary algorithm are outlined, and the evolutionary algorithm indeed discovers rules for the CA which solve these problems efficiently.

Evolution Strategies for Laser Pulse Compression

This study describes first steps taken to bring evolutionaryoptimization technology from computer simulations to real world experimentationin physics laboratories. The approach taken considers a wellunderstood Laser Pulse Compression problem accessible both to simulationand laboratory experimentation as a test function for variants ofEvolution Strategies. The main focus lies on coping with the unavoidablenoise present in laboratory experimentation. Results from simulations arecompared to previous studies and to laboratory experiments.

Evolution strategies for engineering design optimisation

Publisher Summary Computer simulations of complex engineering problems have become a standard tool of modern product development and design. The increasing computational power at modest costs leads to a growing interest in directly using computer simulation codes for automatic product optimization. Traditional numerical optimization methods have some drawbacks that make them difficult to use with complex simulation software. Gradient-based methods are always local optimizers, thus requiring additional methods such as random restarts to find global optima. Evolutionary optimization is a way to overcome some of these limitations. This chapter presents a paper that introduces evolution strategies as a robust and fault-tolerant optimization method, which does not rely on gradients, is easily adaptable to massively parallel computing systems and can be used for single and multiple-criteria optimization. It describes a complex and aerodynamical test problem that was solved by an evolution strategy. This paper introduces the basic elements of evolution strategies and addresses their important features such as self adaptation, robustness, and multiprocessor implementations.