University of Dortmund Reihe Computational Intelligence

UNIVERSITY OF DORTMUND REIHE COMPUTATIONAL INTELLIGENCE COLLABORATIVE RESEARCH CENTER 531 Design and Management of Complex Technical Processes and Systems by means of Computational Intelligence Methods A Uni ed Model of Non-Panmictic Population Structures in Evolutionary Algorithms

TECHNISCHE UNIVERSITÄT DORTMUND REIHE COMPUTATIONAL INTELLIGENCE COLLABORATIVE RESEARCH CENTER 531 Design and Management of Complex Technical Processes and Systems by means of Computational Intelligence Methods Additive Approximations of Pareto-Optimal Sets by Evolutionary Multi-Objective Algorithms

Often the Pareto front of a multi-objective optimization problem grows exponentially with the problem size. In this case, it is not possible to compute the whole Pareto front efficiently and one is interested in good approximations. We consider how evolutionary algorithms can achieve such approximations by using different diversity mechanisms. We discuss some well-known approaches such as the d...

TECHNISCHE UNIVERSITÄT DORTMUND REIHE COMPUTATIONAL INTELLIGENCE COLLABORATIVE RESEARCH CENTER 531 Design and Management of Complex Technical Processes and Systems by means of Computational Intelligence Methods Approximating Minimum Multicuts by Evolutionary Multi-Objective Algorithms

It has been shown that simple evolutionary algorithms are able to solve the minimum cut problem in expected polynomial time when using a multi-objective model of the problem. In this paper, we generalize these ideas to the NP-hard minimum multicut problem. Given a set of k terminal pairs, we prove that evolutionary algorithms in combination with a multi-objective model of the problem are able t...

TECHNISCHE UNIVERSITÄT DORTMUND REIHE COMPUTATIONAL INTELLIGENCE COLLABORATIVE RESEARCH CENTER 531 Design and Management of Complex Technical Processes and Systems by means of Computational Intelligence Methods Theoretical Analysis of Diversity Mechanisms for Global Exploration

Maintaining diversity is important for the performance of evolutionary algorithms. Diversity mechanisms can enhance global exploration of the search space and enable crossover to find dissimilar individuals for recombination. We focus on the global exploration capabilities of mutation-based algorithms. Using a simple bimodal test function and rigorous runtime analyses, we compare well-known div...

TECHNISCHE UNIVERSITÄT DORTMUND REIHE COMPUTATIONAL INTELLIGENCE COLLABORATIVE RESEARCH CENTER 531 Design and Management of Complex Technical Processes and Systems by means of Computational Intelligence Methods An Empirical Investigation of Simplified Step-Size Adapatation in Evolution Strategies with a View to Theory

Randomized direct-search methods for the optimization of a function f : R → R given by a black box for f -evaluations are investigated. We consider the cumulative step-size adaptation (CSA) for the variance of multivariate zero-mean normal distributions. Those are commonly used to sample new candidate solutions within metaheuristics, in particular within the CMA Evolution Strategy (CMA-ES), a s...