Volker Nannen

A method for parameter calibration and relevance estimation in evolutionary algorithms

We present and evaluate a method for estimating the relevance and calibrating the values of parameters of an evolutionary algorithm. The method provides an information theoretic measure on how sensitive a parameter is to the choice of its value. This can be used to estimate the relevance of parameters, to choose between different possible sets of parameters, and to allocate resources to the calibration of relevant parameters. The method calibrates the evolutionary algorithm to reach a high performance, while retaining a maximum of robustness and generalizability. We demonstrate the method on an agent-based application from evolutionary economics and show how the method helps to design an evolutionary algorithm that allows the agents to achieve a high welfare with a minimum of algorithmic complexity.

Costs and Benefits of Tuning Parameters of Evolutionary Algorithms

We present an empirical study on the impact of different design choices on the performance of an evolutionary algorithm (EA). Four EA components are considered--parent selection, survivor selection, recombination and mutation--and for each component we study the impact of choosing the right operator, and of tuning its free parameter(s). We tune 120 different combinations of EA operators to 4 different classes of fitness landscapes, and measure the cost of tuning. We find that components differ greatly in importance. Typically the choice of operator for parent selection has the greatest impact, and mutation needs the most tuning. Regarding individual EAs however, the impact of design choices for one component depends on the choices for other components, as well as on the available amount of resources for tuning.

Efficient relevance estimation and value calibration of evolutionary algorithm parameters

Calibrating the parameters of an evolutionary algorithm (EA) is a laborious task. The highly stochastic nature of an EA typically leads to a high variance of the measurements. The standard statistical method to reduce variance is measurement replication, i.e., averaging over several test runs with identical parameter settings. The computational cost of measurement replication scales with the variance and is often too high to allow for results of statistical significance. In this paper we study an alternative: the REVAC method for Relevance Estimation and Value Calibration, and we investigate how different levels of measurement replication influence the cost and quality of its calibration results. Two sets ofof experiments are reported: calibrating a genetic algorithm on standard benchmark problems, and calibrating a complex simulation in evolutionary agent-based economics. We find that measurement replication is not essential to REVAC, which emerges as a strong and efficient alternative to existing statistical methods.

Parameter calibration using meta-algorithms

Calibrating an evolutionary algorithm (EA) means finding the right values of algorithm parameters for a given problem. This issue is highly relevant, because it has a high impact (the performance of EAs does depend on appropriate parameter values), and it occurs frequently (parameter values must be set before all EA runs). This issue is also highly challenging, because finding good parameter values is a difficult task. In this paper we propose an algorithmic approach to EA calibration by describing a method, called REVAC, that can determine good parameter values in an automated manner on any given problem instance. We validate this method by comparing it with the conventional hand-based calibration and another algorithmic approach based on the classical meta-GA. Comparative experiments on a set of randomly generated problem instances with various levels of multi-modality show that GAs calibrated with REVAC can outperform those calibrated by hand and by the meta-GA.

Variance reduction in meta-EDA

We study the benefit of measurement replication when using the Relevance Estimation and Value Calibration method to calibrate a genetic algorithm. We find that replication is not essential to REVAC, which makes it a strong alternative to existing statistical tools which are computationally costly.