Automatic parameter tuning for Evolutionary Algorithms using a Bayesian Case-Based Reasoning system

• An automatic parameter tuning system for evolutionary algorithms is analyzed.
• The system is based on Bayesian Networks and Case-Based Reasoning methodology.
• The system is compared to an optimal exhaustive statistical analysis.
• The behavior of the system is evaluated in two scenarios: static and dynamic.
• In both scenarios, statistical tests prove the efficiency of the system.

The widespread use and applicability of Evolutionary Algorithms is due in part to the ability to adapt them to a particular problem-solving context by tuning their parameters. This is one of the problems that a user faces when applying an Evolutionary Algorithm to solve a given problem. Before running the algorithm, the user typically has to specify values for a number of parameters, such as population size, selection rate, and probability operators.This paper empirically assesses the performance of an automatic parameter tuning system in order to avoid the problems of time requirements and the interaction of parameters. The system, based on Bayesian Networks and Case-Based Reasoning methodology, estimates the best parameter setting for maximizing the performance of Evolutionary Algorithms. The algorithms are applied to solve a basic problem in constraint-based, geometric parametric modeling, as an instance of general constraint-satisfaction problems.The experimental results demonstrate the validity of the proposed system and its potential effectiveness for configuring algorithms.

Figure optionsDownload as PowerPoint slide