Learn More
Quantifying and comparing performance of numerical optimization algorithms is an important aspect of research in search and optimization. However, this task turns out to be tedious and difficult to realize even in the single-objective case – at least if one is willing to accomplish it in a scientifically decent and rigorous way. The COCO software used for(More)
This paper presents results of the BBOB-2009 benchmarking of 31 search algorithms on 24 noiseless functions in a black-box optimization scenario in continuous domain. The runtime of the algorithms, measured in number of function evaluations, is investigated and a connection between a single convergence graph and the runtime distribution is uncovered.(More)
Quantifying and comparing performance of optimization algorithms is one important aspect of research in search and optimization. However , this task turns out to be tedious and difficult to realize even in the single-objective case – at least if one is willing to accomplish it in a scientifically decent and rigorous way. The BBOB 2009 workshop will furnish(More)
Quantifying and comparing performance of numerical optimization algorithms is one important aspect of research in search and optimization. However, this task turns out to be tedious and difficult to realize even in the single-objective case – at least if one is willing to accomplish it in a scientifically decent and rigorous way. The BBOB 2010 workshop will(More)
This paper presents a performance comparison of 4 direct search strategies in continuous search spaces using the noisy sphere as test function. While the results of the Evolution Strategy (ES), Evolutionary Gradient Search (EGS), Simultaneous Perturbation Stochastic Approximation (SPSA) considered are already known from literature, Implicit Filtering (IF)(More)
This paper analyzes the behavior of the (mu/mu<sub>I</sub>,lambda) ES on a class of noisy positive definite quadratic forms (PDQFs). First the equations for the normalized progress rates are derived and then analyzed for constant normalized noise strength and constant (non-normalized) noise strength. Since in the latter case the strategy is not able to(More)
—This paper describes the algorithm's engineering of a covariance matrix self-adaptation Evolution Strategy (ES) for solving a mixed linear/nonlinear constrained optimization problem arising in portfolio optimization. While the feasible solution space is defined by the (probabilistic) simplex, the nonlinearity comes in by a cardinality constraint bounding(More)