Theory of Evolutionary Algorithms

This report documents the talks and discussions of Dagstuhl Seminar 13271 “Theory of Evolutionary Algorithms”. This seminar, now in its 7th edition, is the main meeting point of the highly active theory of randomized search heuristics subcommunities in Australia, Asia, North America and Europe. Topics intensively discussed include a complexity theory for randomized search heuristics, evolutionary computation in noisy settings, the drift analysis technique, and parallel evolutionary computation.

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[47]  Nicola Beume,et al.  S-Metric Calculation by Considering Dominated Hypervolume as Klee's Measure Problem , 2009, Evolutionary Computation.

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[49]  Dirk Sudholt,et al.  The benefit of migration in parallel evolutionary algorithms , 2010, GECCO '10.

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[51]  Tom Schaul,et al.  Exponential natural evolution strategies , 2010, GECCO '10.

[52]  Dirk Sudholt,et al.  General Scheme for Analyzing Running Times of Parallel Evolutionary Algorithms , 2010, PPSN.

[53]  Frank Neumann,et al.  Set-based multi-objective optimization, indicators, and deteriorative cycles , 2010, GECCO '10.

[54]  Anne Auger,et al.  Comparing results of 31 algorithms from the black-box optimization benchmarking BBOB-2009 , 2010, GECCO '10.

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[57]  Pietro Simone Oliveto,et al.  On the effectiveness of crossover for migration in parallel evolutionary algorithms , 2011, GECCO '11.

[58]  Dirk Sudholt,et al.  Analysis of Speedups in Parallel Evolutionary Algorithms for Combinatorial Optimization - (Extended Abstract) , 2011, ISAAC.

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[63]  Lothar Thiele,et al.  Mutation operator characterization: Exhaustiveness, locality, and bias , 2011, 2011 IEEE Congress of Evolutionary Computation (CEC).

[64]  Marco Laumanns,et al.  On Sequential Online Archiving of Objective Vectors , 2011, EMO.

[65]  Leonardo Vanneschi,et al.  The K landscapes: a tunably difficult benchmark for genetic programming , 2011, GECCO '11.

[66]  Victor L. Selivanov A Fine Hierarchy of ω-Regular k-Partitions , 2011, CiE.

[67]  Rainer Böhme,et al.  Collective Exposure: Peer Effects in Voluntary Disclosure of Personal Data , 2011, Financial Cryptography.

[68]  Dirk Sudholt,et al.  Homogeneous and Heterogeneous Island Models for the Set Cover Problem , 2012, PPSN.

[69]  Thomas Jansen,et al.  Fixed budget computations: a different perspective on run time analysis , 2012, GECCO '12.

[70]  Lorrie Faith Cranor,et al.  Necessary But Not Sufficient: Standardized Mechanisms for Privacy Notice and Choice , 2012, J. Telecommun. High Technol. Law.

[71]  Joyce Fortune,et al.  Using systems thinking to evaluate a major project: The case of the Gateshead Millennium Bridge , 2012 .

[72]  Jörg Lässig,et al.  General Upper Bounds on the Running Time of Parallel Evolutionary Algorithms , 2012, ArXiv.

[73]  Andrew M. Sutton,et al.  The max problem revisited: the importance of mutation in genetic programming , 2012, GECCO '12.

[74]  Subhash Suri,et al.  On Klee's measure problem for grounded boxes , 2012, SoCG '12.

[75]  Arina Buzdalova,et al.  Increasing Efficiency of Evolutionary Algorithms by Choosing between Auxiliary Fitness Functions with Reinforcement Learning , 2012, 2012 11th International Conference on Machine Learning and Applications.

[76]  Willem L. Fouché Algorithmic Randomness and Ramsey Properties of Countable Homogeneous Structures , 2012, WoLLIC.

[77]  M. Angela Sasse,et al.  Privacy is a process, not a PET: a theory for effective privacy practice , 2012, NSPW '12.

[78]  Manuel López-Ibáñez,et al.  Runtime Analysis of Simple Interactive Evolutionary Biobjective Optimization Algorithms , 2012, PPSN.

[79]  Willem L. Fouché Martin-Löf Randomness, Invariant Measures and Countable Homogeneous Structures , 2012, Theory of Computing Systems.

[80]  P. Stadler,et al.  Landscape Encodings Enhance Optimization , 2011, PloS one.

[81]  Benjamin Doerr,et al.  Lessons from the black-box: fast crossover-based genetic algorithms , 2013, GECCO '13.

[82]  Mai Gehrke Stone duality, topological algebra, and recognition , 2013, ArXiv.

[83]  Karl Bringmann,et al.  Bringing Order to Special Cases of Klee's Measure Problem , 2013, MFCS.

[84]  Olivier Teytaud,et al.  Noisy optimization complexity under locality assumption , 2013, FOGA XII '13.

[85]  Sabrina Sauer,et al.  User innovativeness in living laboratories: everyday user improvisations with ICTs as a source of innovation , 2013 .

[86]  Olivier Teytaud,et al.  Noisy optimization convergence rates , 2013, GECCO '13 Companion.

[87]  Ian Brown Lawful Interception Capability Requirements , 2013 .

[88]  Alberto Moraglio,et al.  Runtime analysis of mutation-based geometric semantic genetic programming on boolean functions , 2013, FOGA XII '13.

[89]  Timothy M. Chan Klee's Measure Problem Made Easy , 2013, 2013 IEEE 54th Annual Symposium on Foundations of Computer Science.

[90]  Joshua D. Knowles,et al.  On Handling Ephemeral Resource Constraints in Evolutionary Search , 2013, Evolutionary Computation.

[91]  Jens Grossklags,et al.  An online experiment of privacy authorization dialogues for social applications , 2013, CSCW.

[92]  Verónica Becher,et al.  A polynomial-time algorithm for computing absolutely normal numbers , 2013, Inf. Comput..

[93]  Thomas Jansen,et al.  Understanding randomised search heuristics lessons from the evolution of theory: A case study , 2014 .

[94]  Dirk Sudholt,et al.  General Upper Bounds on the Runtime of Parallel Evolutionary Algorithms* , 2014, Evolutionary Computation.

[95]  Jing Qin,et al.  Geometry and Coarse-Grained Representations of Landscapes , 2014 .

[96]  Philipp Schlicht,et al.  Wadge-like reducibilities on arbitrary quasi-Polish spaces , 2012, Mathematical Structures in Computer Science.

[97]  Michael R. Bussieck,et al.  PAVER 2.0: an open source environment for automated performance analysis of benchmarking data , 2014, J. Glob. Optim..

[98]  Rolf Wanka,et al.  How Much Forcing Is Necessary to Let the Results of Particle Swarms Converge? , 2014, ICSIBO.

[99]  Zbigniew Michalewicz,et al.  A comprehensive benchmark set and heuristics for the traveling thief problem , 2014, GECCO.

[100]  Frank Neumann,et al.  On the Runtime of Randomized Local Search and Simple Evolutionary Algorithms for Dynamic Makespan Scheduling , 2015, IJCAI.

[101]  Rolf Wanka,et al.  Explanation of Stagnation at Points that are not Local Optima in Particle Swarm Optimization by Potential Analysis , 2015, GECCO.

[102]  Nicholas I. M. Gould,et al.  CUTEst: a Constrained and Unconstrained Testing Environment with safe threads for mathematical optimization , 2013, Computational Optimization and Applications.

[103]  Benjamin Doerr,et al.  Runtime Analysis of (1+1) Evolutionary Algorithm Controlled with Q-learning Using Greedy Exploration Strategy on OneMax+ZeroMax Problem , 2015, EvoCOP.

[104]  Rolf Wanka,et al.  Particle swarm optimization almost surely finds local optima , 2013, GECCO '13.

[105]  Yann Ollivier,et al.  Laplace's Rule of Succession in Information Geometry , 2015, GSI.

[106]  Carsten Witt,et al.  Population Size vs. Mutation Strength for the (1+λ) EA on OneMax , 2015, GECCO.