Tracking Moving Optima Using Kalman-Based Predictions

The dynamic optimization problem concerns finding an optimum in a changing environment. In the field of evolutionary algorithms, this implies dealing with a timechanging fitness landscape. In this paper we compare different techniques for integrating motion information into an evolutionary algorithm, in the case it has to follow a time-changing optimum, under the assumption that the changes follow a nonrandom law. Such a law can be estimated in order to improve the optimum tracking capabilities of the algorithm. In particular, we will focus on first order dynamical laws to track moving objects. A vision-based tracking robotic application is used as testbed for experimental comparison. Keywords Evolutionary algorithms, vision-based tracking, dynamic optimization problem, timevarying fitness function.

• Type: Journal
• Year: 2008
• Where: EC
• Authors: Claudio Rossi, Mohamed Abderrahim, Julio César Díaz
• Keywords: Dynamic Optimization Problem, EC 2008, ECommerce, Evolutionary Algorithms, Timechanging Fitness Landscape