A general-purpose tunable landscape generator

M. Gallagher, Bo Yuan
2006 IEEE Transactions on Evolutionary Computation  
The research literature on metaheuristic and evolutionary computation has proposed a large number of algorithms for the solution of challenging real-world optimization problems. It is often not possible to study theoretically the performance of these algorithms unless significant assumptions are made on either the algorithm itself or the problems to which it is applied, or both. As a consequence, metaheuristics are typically evaluated empirically using a set of test problems. Unfortunately,
more » ... tively little attention has been given to the development of methodologies and tools for the large-scale empirical evaluation and/or comparison of metaheuristics. In this paper, we propose a landscape (test-problem) generator that can be used to generate optimization problem instances for continuous, bound-constrained optimization problems. The landscape generator is parameterized by a small number of parameters, and the values of these parameters have a direct and intuitive interpretation in terms of the geometric features of the landscapes that they produce. An experimental space is defined over algorithms and problems, via a tuple of parameters for any specified algorithm and problem class (here determined by the landscape generator). An experiment is then clearly specified as a point in this space, in a way that is analogous to other areas of experimental algorithmics, and more generally in experimental design. Experimental results are presented, demonstrating the use of the landscape generator. In particular, we analyze some simple, continuous estimation of distribution algorithms, and gain new insights into the behavior of these algorithms using the landscape generator. Index Terms-Continuous optimization, empirical algorithm analysis, estimation of distribution algorithm, test-problem generator. He is currently a Lecturer in the School of Information Technology and Electrical Engineering, University of Queensland. His main research interests are metaheuristic optimization and machine learning algorithms, in particular, techniques based on statistical modeling. He is also interested in the biologically inspired algorithms, methodology for empirical evaluation of algorithms, and the visualization of high-dimensional data.
doi:10.1109/tevc.2005.863628 fatcat:r3nawkarbvbl5mixhitzo7in2u