Lipschitzian optimization without the Lipschitz constant
Journal of Optimization Theory and Applications
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
A Survey of Optimization by Building and Using Probabilistic Models
Computational Optimization and Applications
Experimental Evaluation of Heuristic Optimization Algorithms: A Tutorial
Journal of Heuristics
ACM Transactions on Mathematical Software (TOMS)
Experimental Research in Evolutionary Computation: The New Experimentalism (Natural Computing Series)
A new class of test functions for global optimization
Journal of Global Optimization
Communications of the ACM
Towards unbiased benchmarking of evolutionary and hybrid algorithms for real-valued optimisation
Connection Science - Evolutionary Learning and Optimisation
Enhancing the Performance of Maximum---Likelihood Gaussian EDAs Using Anticipated Mean Shift
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Convergence analysis of UMDAC with finite populations: a case study on flat landscapes
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Optimisation and generalisation: footprints in instance space
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Benchmarking evolutionary algorithms: towards exploratory landscape analysis
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Understanding TSP difficulty by learning from evolved instances
LION'10 Proceedings of the 4th international conference on Learning and intelligent optimization
Experimental Methods for the Analysis of Optimization Algorithms
Experimental Methods for the Analysis of Optimization Algorithms
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
A general-purpose tunable landscape generator
IEEE Transactions on Evolutionary Computation
Evolving Problems to Learn About Particle Swarm Optimizers and Other Search Algorithms
IEEE Transactions on Evolutionary Computation
Hi-index | 0.00 |
In this paper we extend a previously proposed randomized landscape generator in combination with a comparative experimental methodology to study the behavior of continuous metaheuristic optimization algorithms. In particular, we generate two-dimensional landscapes with parameterized, linear ridge structure, and perform pairwise comparisons of algorithms to gain insight into what kind of problems are easy and difficult for one algorithm instance relative to another. We apply this methodology to investigate the specific issue of explicit dependency modeling in simple continuous estimation of distribution algorithms. Experimental results reveal specific examples of landscapes (with certain identifiable features) where dependency modeling is useful, harmful, or has little impact on mean algorithm performance. Heat maps are used to compare algorithm performance over a large number of landscape instances and algorithm trials. Finally, we perform a meta-search in the landscape parameter space to find landscapes which maximize the performance between algorithms. The results are related to some previous intuition about the behavior of these algorithms, but at the same time lead to new insights into the relationship between dependency modeling in EDAs and the structure of the problem landscape. The landscape generator and overall methodology are quite general and extendable and can be used to examine specific features of other algorithms.