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)
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
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
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In this paper we extend a previously proposed randomized landscape generator in combination with a comparative experimental methodology to study the behaviour of continuous metaheuristic optimization algorithms. In particular, we generate landscapes with parameterised, 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 modelling in simple continuous Estimation of Distribution Algorithms. Experimental results reveal specific examples of landscapes (with certain identifiable features) where dependency modelling is useful, harmful or has little impact on average algorithm performance. The results are related to some previous intuition about the behaviour of these algorithms, but at the same time lead to new insights into the relationship between dependency modelling in EDAs and the structure of the problem landscape. The overall methodology is quite general and could be used to examine specific features of other algorithms.