6 citations · 15 across the 5 of their papers we have counts for
9 papers · 1 filter
Theoretical Study of Optimizing Rugged Landscapes with the cGA
Tobias Friedrich, Timo Kötzing, Frank Neumann +1
Estimation of distribution algorithms (EDAs) provide a distribution - based approach for optimization which adapts its probability distribution during the run of the algorithm. We…
Improved Fixed-Budget Results via Drift Analysis
Timo Kötzing, Carsten Witt
Fixed-budget theory is concerned with computing or bounding the fitness value achievable by randomized search heuristics within a given budget of fitness function evaluations. Desp…
Bounding Bloat in Genetic Programming
Benjamin Doerr, Timo Kötzing, J. A. Gregor Lagodzinski +1
While many optimization problems work with a fixed number of decision variables and thus a fixed-length representation of possible solutions, genetic programming (GP) works on vari…
Ring Migration Topology Helps Bypassing Local Optima
Clemens Frahnow, Timo Kötzing
Running several evolutionary algorithms in parallel and occasionally exchanging good solutions is referred to as island models. The idea is that the independence of the different i…
Destructiveness of Lexicographic Parsimony Pressure and Alleviation by a Concatenation Crossover in Genetic Programming
Timo Kötzing, J. A. Gregor Lagodzinski, Johannes Lengler +1
For theoretical analyses there are two specifics distinguishing GP from many other areas of evolutionary computation. First, the variable size representations, in particular yieldi…
The Right Mutation Strength for Multi-Valued Decision Variables
Benjamin Doerr, Carola Doerr, Timo Kötzing
The most common representation in evolutionary computation are bit strings. This is ideal to model binary decision variables, but less useful for variables taking more values. With…