
Bläsius, Thomas; Radermacher, Marcel; Rutter, Ignaz How to Draw a Planarization. Software Seminar (SOFSEM) 2017: 295308
We study the problem of computing straightline drawings of nonplanar graphs with few crossings. We assume that a crossingminimization algorithm is applied first, yielding a planarization, i.e., a planar graph with a dummy vertex for each crossing, that fixes the topology of the resulting drawing. We present and evaluate two different approaches for drawing a planarization in such a way that the edges of the input graph are as straight as possible. The first approach is based on the planaritypreserving forcedirected algorithm ImPrEd, the second approach, which we call Geometric Planarization Drawing, iteratively moves vertices to their locally optimal positions in the given initial drawing.

Chauhan, Ankit; Friedrich, Tobias; Quinzan, Francesco Approximating Optimization Problems using EAs on ScaleFree Networks. Genetic and Evolutionary Computation Conference (GECCO) 2017
It has been experimentally observed that realworld networks follow certain topologicalproperties, such as smallworld, powerlaw etc. To study these networks, many random graph models, such as Preferential Attachment, have been proposed. In this paper, we consider the deterministic properties which capture powerlaw degree distribution and degeneracy. Networks with these properties are known as scalefree networks in the literature. Many interesting problems remain NPhard on scalefree networks. We study the relationship between scalefree properties and the approximationratio of some commonly used evolutionary algorithms. For the Vertex Cover, we observe experimentally that the ((1+1)\) EA always gives the better result than a greedy local search, even when it runs for only (O(n log(n))\) steps. We give the construction of a scalefree network in which a multiobjective algorithm and a greedy algorithm obtain optimal solutions, while the ((1+1)\) EA obtains the worst possible solution with constant probability. We prove that for the Dominating Set, Vertex Cover, Connected Dominating Set and Independent Set, the ((1+1)\) EA obtains constantfactor approximation in expected run time (O(n log(n))\) and (O(n^4)\) respectively. Whereas, GSEMO gives even better approximation than ((1+1)\) EA in expected run time (O(n3)\) for Dominating Set, Vertex Cover and Connected Dominating Set on such networks.

Doerr, Benjamin; Doerr, Carola; Kötzing, Timo Unknown Solution Length Problems With No Asymptotically Optimal Run Time. Genetic and Evolutionary Computation Conference (GECCO) 2017
We revisit the problem of optimizing a fitness function of unknown dimension; that is, we face a function defined over bitstrings of large length (N\), but only (n << N\) of them have an influence on the fitness. Neither the position of these relevant bits nor their number is known. In previous work, variants of the ((1 + 1)\) evolutionary algorithm (EA) have been developed that solve, for arbitrary s ∈ N, such OneMax and LeadingOnes instances, simultaneously for all n ∈ N, in expected time (O(n(log(n))^2 log log(n) dots log^(s−1) (n)(log^(s) (n))^1+ε )\)and (O(n^2 log(n) log log(n) dots log^(s−1) (n)(log^(s) (n))^1+ε )\), respectively; that is, in almost the same time as if n and the relevant bit positions were known. In this work, we prove the first, almost matching, lower bounds for this setting. For LeadingOnes, we show that, for every (s in N\), the ((1 + 1)\) EA with any mutation operator treating zeros and ones equally has an expected run time of (omega(n^2 log(n) log log(n) dots log^(s) (n)) when facing problem size (n\). Aiming at closing the small remaining gap, we realize that, quite surprisingly, there is no asymptotically best performance. For any algorithm solving, for all (n\), all instances of size (n\) in expected time at most (T (n)\), there is an algorithm doing the same in time (T'(n)\) with (T' = o(T )\). For OneMax we show results of similar flavor.

Doerr, Benjamin; Fischbeck, Philipp; Frahnow, Clemens; Friedrich, Tobias; Kötzing, Timo; Schirneck, Martin Island Models Meet Rumor Spreading. Genetic and Evolutionary Computation Conference (GECCO) 2017
Island models in evolutionary computation solve problems by a careful interplay of independently running evolutionary algorithms on the island and an exchange of good solutions between the islands. In this work, we conduct rigorous run time analyses for such island models trying to simultaneously obtain good run times and low communication effort. We improve the existing upper bounds for the communication effort (i) by improving the run time bounds via a careful analysis, (ii) by setting the balance between individual computation and communication in a more appropriate manner, and (iii) by replacing the usual communicatewithallneighbors approach with randomized rumor spreading, where each island contacts a randomly chosen neighbor. This epidemic communication paradigm is known to lead to very fast and robust information dissemination in many applications. Our results concern islands running simple (1+1) evolutionary algorithms, we regard ddimensional tori and complete graphs as communication topologies, and optimize the classic test functions OneMax and LeadingOnes.

Doerr, Benjamin; Kötzing, Timo; Lagodzinski, J. A. Gregor; Lengler, Johannes Bounding Bloat in Genetic Programming. Genetic and Evolutionary Computation Conference (GECCO) 2017
While many optimization problems work with a fixed number of decision variables and thus a fixedlength representation of possible solutions, genetic programming (GP) works on variablelength representations. A naturally occurring problem is that of bloat (unnecessary growth of solutions) slowing down optimization. Theoretical analyses could so far not bound bloat and required explicit assumptions on the magnitude of bloat. In this paper we analyze bloat in mutationbased genetic programming for the two test functions ORDER and MAJORITY. We overcome previous assumptions on the magnitude of bloat and give matching or closetomatching upper and lower bounds for the expected optimization time. In particular, we show that the ((1+1)\) GP takes (i) (Theta(T_init + n log n)\) iterations with bloat control on ORDER as well as MAJORITY; and (ii) (O(T_init log T_init + n(log n)^3)\) and (Omega(T_init + n log n)\) (and (Omega(T_init log T_init)\) for (n = 1\)) iterations without bloat control on MAJORITY.

Friedrich, Tobias; Kötzing, Timo; Lagodzinski, J. A. Gregor; Neumann, Frank; Schirneck, Martin Analysis of the (1+1) EA on Subclasses of Linear Functions under Uniform and Linear Constraints. Foundations of Genetic Algorithms (FOGA) 2017: 4554
Linear functions have gained a lot of attention in the area of run time analysis of evolutionary computation methods and the corresponding analyses have provided many effective tools for analyzing more complex problems. In this paper, we consider the behavior of the classical (1+1) Evolutionary Algorithm for linear functions under linear constraint. We show tight bounds in the case where both the objective function and the constraint is given by the OneMax function and present upper bounds as well as lower bounds for the general case. Furthermore, we also consider the LeadingOnes fitness function.

Friedrich, Tobias; Kötzing, Timo; Melnichenko, Anna Analyzing Search Heuristics with Differential Equations. Genetic and Evolutionary Computation Conference (GECCO) 2017
Drift Theory is currently the most common technique for the analysis of randomized search heuristics because of its broad applicability and the resulting tight first hitting time bounds. The biggest problem when applying a drift theorem is to find a suitable potential function which maps a complex space into a single number, capturing the essence of the state of the search in just one value. We discuss another method for the analysis of randomized search heuristics based on the Theory of Differential Equations. This method considers the deterministic counterpart of the randomized process by replacing probabilistic outcomes by their expectation, and then bounding the error with good probability. We illustrate this by analyzing an Ant Colony Optimization algorithm (ACO) for the Minimum Spanning Tree problem (MST).

Friedrich, Tobias; Kötzing, Timo; Quinzan, Francesco; Sutton, Andrew Michael Resampling vs Recombination: a Statistical Run Time Estimation. Foundations of Genetic Algorithms (FOGA) 2017: 2536
Noise is pervasive in realworld optimization, but there is still little understanding of the interplay between the operators of randomized search heuristics and explicit noisehandling techniques, such as statistical resampling. In this paper, we report on several statistical models and theoretical results that help to clarify this reciprocal relationship for a collection of randomized search heuristics on noisy functions. We consider the optimization of pseudoBoolean functions under additive posterior Gaussian noise and explore the tradeo between noise reduction and the computational cost of resampling. We first perform experiments to find the optimal parameters at a given noise intensity for a mutationonly evolutionary algorithm, a genetic algorithm employing recombination, an estimation of distribution algorithm (EDA), and an ant colony optimization algorithm. We then observe how the optimal parameter depends on the noise intensity for the different algorithms. Finally, we locate the point where statistical resampling costs more than it is worth in terms of run time. We find that the EA requires the highest number of resamples to obtain the best speedup, whereas crossover reduces both the run time and the number of resamples required. Most surprisingly, we find that EDAlike algorithms require no resampling, and can handle noise implicitly.

Friedrich, Tobias; Kötzing, Timo; Wagner, Markus A Generic BetandRun Strategy for Speeding Up Stochastic Local Search. Association for the Advancement of Artificial Intelligence (AAAI) 2017: 801807
A common strategy for improving optimization algorithms is to restart the algorithm when it is believed to be trapped in an inferior part of the search space. However, while specific restart strategies have been developed for specific problems (and specific algorithms), restarts are typically not regarded as a general tool to speed up an optimization algorithm. In fact, many optimization algorithms do not employ restarts at all. Recently, "betandrun" was introduced in the context of mixedinteger programming, where first a number of short runs with randomized initial conditions is made, and then the most promising run of these is continued. In this article, we consider two classical NPcomplete combinatorial optimization problems, traveling salesperson and minimum vertex cover, and study the effectiveness of different betandrun strategies. In particular, our restart strategies do not take any problem knowledge into account, nor are tailored to the optimization algorithm. Therefore, they can be used offtheshelf. We observe that stateoftheart solvers for these problems can benefit significantly from restarts on standard benchmark instances.

Friedrich, Tobias; Krohmer, Anton; Rothenberger, Ralf; Sutton, Andrew M. Phase Transitions for ScaleFree SAT Formulas. Association for the Advancement of Artificial Intelligence (AAAI) 2017: 38933899
Recently, a number of nonuniform random satisfiability models have been proposed that are closer to practical satisfiability problems in some characteristics. In contrast to uniform random Boolean formulas, scalefree formulas have a variable occurrence distribution that follows a power law. It has been conjectured that such a distribution is a more accurate model for some industrial instances than the uniform random model. Though it seems that there is already an awareness of a threshold phenomenon in such models, there is still a complete picture lacking. In contrast to the uniform model, the critical density threshold does not lie at a single point, but instead exhibits a functional dependency on the powerlaw exponent. For scalefree formulas with clauses of length (k = 2\), we give a lower bound on the phase transition threshold as a function of the scaling parameter. We also perform computational studies that suggest our bound is tight and investigate the critical density for formulas with higher clause lengths. Similar to the uniform model, on formulas with (k ge 3\), we find that the phase transition regime corresponds to a set of formulas that are difficult to solve by backtracking search.

Friedrich, Tobias; Neumann, Frank What’s Hot in Evolutionary Computation. Association for the Advancement of Artificial Intelligence (AAAI) 2017: 50645066
We provide a brief overview on some hot topics in the area of evolutionary computation. Our main focus is on recent developments in the areas of combinatorial optimization and realworld applications. Furthermore, we highlight recent progress on the theoretical understanding of evolutionary computing methods.

Gao, Wanru; Friedrich, Tobias; Kötzing, Timo; Neumann, Frank Scaling up Local Search for Minimum Vertex Cover in Large Graphs by Parallel Kernelization. Australasian Conference on Artificial Intelligence (AUSAI) 2017
We investigate how wellperforming local search algorithms for small or medium size instances can be scaled up to perform well for large inputs. We introduce a parallel kernelization technique that is motivated by the assumption that graphs in medium to large scale are composed of components which are on their own easy for stateoftheart solvers but when hidden in large graphs are hard to solve. To show the effectiveness of our kernelization technique, we consider the wellknown minimum vertex cover problem and two stateoftheart solvers called NuMVC and FastVC. Our kernelization approach reduces an existing large problem instance significantly and produces better quality results on a wide range of benchmark instances and real world graphs.

Katzmann, Maximilian; Komusiewicz, Christian Systematic Exploration of Larger Local Search Neighborhoods for the Minimum Vertex Cover Problem. Association for the Advancement of Artificial Intelligence (AAAI) 2017: 846852
We investigate the potential of exhaustively exploring larger neighborhoods in local search algorithms for Minimum Vertex Cover. More precisely, we study whether, for moderate values of (k\), it is feasible and worthwhile to determine, given a graph (G\) with vertex cover (C\), if there is a (k\)swap (S\) such that ((C setminus S) cup (S setminus C)\) is a smaller vertex cover of (G\). First, we describe an algorithm running in (Delta^O(k) cdot n\) time for searching the (k\)swap neighborhood on (n\)vertex graphs with maximum degree (\Delta\). Then, we demonstrate that, by devising additional pruning rules that decrease the size of the search space, this algorithm can be implemented so that it solves the problem quickly for (k approx 20\). Finally, we show that it is worthwhile to consider moderatelysized (k\)swap neighborhoods. For our benchmark data set, we show that when combining our algorithm with a hillclimbing approach, the solution quality improves quickly with the radius (k\) of the local search neighborhood and that in most cases optimal solutions can be found by setting (k = 21\).

Kovacs, Robert; Seufert, Anna; Wall, Ludwig; Chen, HsiangTing; Meinel, Florian; Müller, Willi; You, Sijing; Brehm, Maximilian; Striebel, Jonathan; Kommana, Yannis; Popiak, Alexander; Bläsius, Thomas; Baudisch, Patrick TrussFab: Fabricating Sturdy LargeScale Structures on Desktop 3D Printers. Human Factors in Computing Systems (CHI) 2017: 26062616
We present TrussFab, an integrated endtoend system that allows users to fabricate large scale structures that are sturdy enough to carry human weight. TrussFab achieves the large scale by complementing 3D print with plastic bottles. It does not use these bottles as “bricks” though, but as beams that form structurally sound nodelink structures, also known as trusses, allowing it to handle the forces resulting from scale and load. TrussFab embodies the required engineering knowledge, allowing nonengineers to design such structures and to validate their design using integrated structural analysis. We have used TrussFab to design and fabricate tables and chairs, a 2.5 m long bridge strong enough to carry a human, a functional boat that seats two, and a 5 m diameter dome.

Krejca, Martin S.; Witt, Carsten Lower Bounds on the Run Time of the Univariate Marginal Distribution Algorithm on OneMax. Foundations of Genetic Algorithms (FOGA) 2017: 6580
The Univariate Marginal Distribution Algorithm (UMDA), a popular estimation of distribution algorithm, is studied from a run time perspective. On the classical OneMax benchmark function, a lower bound of (Omega(mu sqrt n + n log n)\), where (\mu\) is the population size, on its expected run time is proved. This is the first direct lower bound on the run time of the UMDA. It is stronger than the bounds that follow from general blackbox complexity theory and is matched by the run time of many evolutionary algorithms. The results are obtained through advanced analyses of the stochastic change of the frequencies of bit values maintained by the algorithm, including carefully designed potential functions. These techniques may prove useful in advancing the field of run time analysis for estimation of distribution algorithms in general.

Pourhassan, Mojgan; Friedrich, Tobias; Neumann, Frank On the Use of the Dual Formulation for Minimum Weighted Vertex Cover in Evolutionary Algorithms. Foundations of Genetic Algorithms (FOGA) 2017: 3744
We consider the weighted minimum vertex cover problem and investigate how its dual formulation can be exploited to design evolutionary algorithms that provably obtain a 2approximation. Investigating multivalued representations, we show that variants of randomized local search and the (1+1) EA achieve this goal in expected pseudopolynomial time. In order to speed up the process, we consider the use of step size adaptation in both algorithms and show that RLS obtains a 2approximation in expected polynomial time while the (1+1) EA still encounters a pseudopolynomial lower bound.

Shi, Feng; Schirneck, Martin; Friedrich, Tobias; Kötzing, Timo; Neumann, Frank Reoptimization Times of Evolutionary Algorithms on Linear Functions Under Dynamic Uniform Constraints. Genetic and Evolutionary Computation Conference (GECCO) 2017
Thee investigations of linear pseudoBoolean functions play a central role in the area of runtime analysis of evolutionary computing techniques. Having an additional linear constraint on a linear function is equivalent to the NPhard knapsack problem and special problem classes thereof have been investigated in recent works. In this paper, we extend these studies to problems with dynamic constraints and investigate the runtime of different evolutionary algorithms to recompute an optimal solution when the constraint bound changes by a certain amount. We study the classical ((1+1)\) EA and populationbased algorithms and show that they recompute an optimal solution very efficiently. Furthermore, we show that a variant of the ((1+(lambda, \lambda))\) GA can recompute the optimal solution more eciently in some cases.

Wagner, Markus; Friedrich, Tobias; Lindauer, Marius Improving local search in a minimum vertex cover solver for classes of networks. Congress on Evolutionary Computation (CEC) 2017
For the minimum vertex cover problem, a wide range of solvers has been proposed over the years. Most classical exact approaches are encountering run time issues on massive graphs that are considered nowadays. A straightforward alternative approach is then to use heuristics, which make assumptions about the structure of the studied graphs. These assumptions are typically hardcoded and are hoped to work well for a wide range of networks  which is in conflict with the nature of broad benchmark sets. With this article, we contribute in two ways. First, we identify a component in an existing solver that influences its performance depending on the class of graphs, and we then customize instances of this solver for different classes of graphs. Second, we create the first algorithm portfolio for the minimum vertex cover to further improve the performance of a single integrated approach to the minimum vertex cover problem.