Per Kristian Lehre

Research output

Research output per year 2003 2007 2009 2010 2011 2014 2015 2016 2018 2019 2022 2023 2024

• 41 Conference contribution
• 23 Article
• 14 Paper
• 2 Commissioned report
• 3 More
  • 2 Chapter
  • 1 Chapter (peer-reviewed)

Research output per year

Research output per year

Search results

• Conference contribution

  Analysis of a Pairwise Dominance Coevolutionary Algorithm And DefendIt

  Lehre, P. K., Fajardo, M. H., Toutouh, J., Hemberg, E. & O'Reilly, U-M., 12 Jul 2023, GECCO '23: Proceedings of the Genetic and Evolutionary Computation Conference. Association for Computing Machinery (ACM), p. 1027-1035 9 p. (GECCO: Genetic and Evolutionary Computation Conference).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Population Density 100%
  • Animation 82%
  • Dynamic models 66%

• Ant Colony Optimization and the Minimum Cut Problem

  Kötzing, T., Lehre, P., Neumann, F. & Oliveto, P., 11 Jul 2010, Proceedings of the 12th annual conference on Genetic and evolutionary computation. p. 1393-1400 8 p.

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Ant colony optimization 100%
  • Combinatorial optimization 16%
  • Polynomials 11%
  15 Citations (Scopus)

• A runtime analysis of simple hyper-heuristics: To mix or not to mix operators

  Lehre, P. K. & Özcan, E., 2013, FOGA 2013 - Proceedings of the 12th ACM Workshop on Foundations of Genetic Algorithms. p. 97-104 8 p.

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Hyper-heuristics 100%
  • Runtime Analysis 91%
  • Heuristics 39%
  • Operator 26%
  • Mixing Distribution 15%
  39 Citations (Scopus)

• Bicriteria optimisation of average and worst-case performance using coevolutionary algorithms

  Benford, A., Olhofer, M., Rodemann, T. & Lehre, P. K., 18 Mar 2024, (Accepted/In press) 2024 IEEE Congress on Evolutionary Computation (CEC). IEEE, (Congress on Evolutionary Computation).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Electric vehicles 100%
  • Renewable energy resources 88%
  • Controllers 59%

• Black-box complexity of parallel search with distributed populations

  Badkobeh, G., Lehre, P. K. & Sudholt, D., 17 Jan 2015, FOGA 2015 - Proceedings of the 2015 ACM Conference on Foundations of Genetic Algorithms XIII. Association for Computing Machinery , p. 3-15 13 p.

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Black Box 100%
  • Communication 40%
  • Topology 30%
  • Premature Convergence 23%
  • Island Model 22%
  13 Citations (Scopus)

• Black-box search by unbiased variation

  Lehre, P. K. & Witt, C., 2010, Proceedings of the 12th Annual Genetic and Evolutionary Computation Conference, GECCO '10. p. 1441-1448 8 p.

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Black Box 100%
  • Heuristic Search 72%
  • Model Complexity 24%
  • Polynomial Complexity 23%
  • Gap Function 23%
  40 Citations (Scopus)

• Dynamic evolutionary optimisation: an analysis of frequency and magnitude of change

  Rohlfshagen, P., Lehre, P. & Yao, X., 12 Jul 2009, Proceedings of the 11th Annual conference on Genetic and evolutionary computation. p. 1713-1720 8 p.

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Boolean functions 100%
  • Set theory 51%
  50 Citations (Scopus)

• Efficient optimisation of noisy fitness functions with population-based evolutionary algorithms

  Dang, D. C. & Lehre, P. K., 17 Jan 2015, FOGA 2015 - Proceedings of the 2015 ACM Conference on Foundations of Genetic Algorithms XIII. Association for Computing Machinery , p. 62-68 7 p.

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Fitness Function 100%
  • Evolutionary Algorithms 90%
  • Partial Information 61%
  • Optimization 57%
  • Optimise 52%
  27 Citations (Scopus)

• Emergence of diversity and its benefits for crossover in genetic algorithms

  Dang, D. C., Friedrich, T., Kötzing, T., Krejca, M. S., Lehre, P. K., Oliveto, P. S., Sudholt, D. & Sutton, A. M., 31 Aug 2016, Parallel Problem Solving from Nature - 14th International Conference, PPSN 2016, Proceedings. Springer Verlag, Vol. 9921 LNCS. p. 890-900 11 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 9921 LNCS).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  Open Access

  • Crossover 100%
  • Genetic Algorithm 93%
  • Genetic algorithms 60%
  • Population Diversity 46%
  • Premature Convergence 45%
  13 Citations (Scopus)

• Escaping local optima with diversity mechanisms and crossover

  Dang, D. C., Friedrich, T., Kötzing, T., Krejca, M. S., Lehre, P. K., Oliveto, P. S., Sudholt, D. & Sutton, A. M., 20 Jul 2016, GECCO 2016 - Proceedings of the 2016 Genetic and Evolutionary Computation Conference. Association for Computing Machinery , p. 645-652 8 p.

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Crossover 100%
  • Gas 80%
  • Population Diversity 70%
  • Island Model 67%
  • Crossover Operator 62%
  26 Citations (Scopus)

• Evolution under partial information

  Dang, D. C. & Lehre, P. K., 2014, GECCO 2014 - Proceedings of the 2014 Genetic and Evolutionary Computation Conference. Association for Computing Machinery , p. 1359-1366 8 p.

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Partial Information 100%
  • Evolutionary Algorithms 97%
  • Fitness 92%
  • Partial Evaluation 85%
  • Evolutionary algorithms 74%
  11 Citations (Scopus)

• Faster black-box algorithms through higher arity operators

  Doerr, B., Johannsen, D., Kötzing, T., Lehre, P. K., Wagner, M. & Winzen, C., 2011, FOGA'11 - Proceedings of the 2011 ACM/SIGEVO Foundations of Genetic Algorithms XI. p. 163-171 9 p.

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Black Box 100%
  • Unary 48%
  • Operator 40%
  • Binary 32%
  • Decrease 19%
  48 Citations (Scopus)

• Fast non-elitist evolutionary algorithms with power-law ranking selection

  Dang, D-C., Eremeev, A., Lehre, P. K. & Qin, X., 8 Jul 2022, GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference. Fieldsend, J. E. (ed.). New York: Association for Computing Machinery (ACM), p. 1372-1380 9 p. (GECCO: Genetic and Evolutionary Computation Conference).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  Open Access

  File

  • Evolutionary Algorithms 100%
  • Ranking 85%
  • Power Law 83%
  • Evolutionary algorithms 76%
  • Mutation 27%
  156 Downloads (Pure)

• Fitness-levels for non-elitist populations

  Lehre, P. K., 2011, Genetic and Evolutionary Computation Conference, GECCO'11. p. 2075-2082 8 p.

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Evolutionary Algorithms 100%
  • Fitness 94%
  • Benchmark 27%
  • Sufficient 20%
  • Lower bound 18%
  50 Citations (Scopus)

• How Fitness Aggregation Methods Affect the Performance of Competitive CoEAs on Bilinear Problems

  Hevia Fajardo, M. & Lehre, P. K., 12 Jul 2023, GECCO '23: Proceedings of the Genetic and Evolutionary Computation Conference. Association for Computing Machinery (ACM), p. 1593-1601 9 p. (GECCO: Genetic and Evolutionary Computation Conference).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  Open Access

  File

  • Evolutionary algorithms 100%
  • Agglomeration 94%
  4 Downloads (Pure)

• Improved runtime bounds for the univariate marginal distribution algorithm via anti-concentration

  Lehre, P. K. & Nguyen, P. T. H., 1 Jul 2017, GECCO '17 Proceedings of the Genetic and Evolutionary Computation Conference. Association for Computing Machinery , p. 1383-1390 8 p.

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  Open Access

  File

  • Estimation of Distribution Algorithms 100%
  • Marginal Distribution 64%
  • Univariate 55%
  • Performance Optimization 26%
  • Epistasis 23%
  27 Citations (Scopus)

  222 Downloads (Pure)

• Level-based analysis of genetic algorithms and other search processes

  Corus, D., Dang, D. C., Eremeev, A. V. & Lehre, P. K., 2014, Parallel Problem Solving from Nature – PPSN XIII: 13th International Conference, Ljubljana, Slovenia, September 13-17, 2014. Proceedings. Springer, p. 912-921 10 p. (Lecture Notes in Computer Science; vol. 8672).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Genetic Algorithm 100%
  • Sorting 90%
  • Mathematical operators 84%
  • Evolutionary algorithms 84%
  • Unary 67%
  27 Citations (Scopus)

• Level-based analysis of the population-based incremental learning algorithm

  Lehre, P. K. & Nguyen, P. T. H., 5 Oct 2018, Proceedings of the 15th International Conference on Parallel Problem Solving from Nature 2018 (PPSN XV). 1 ed. Springer, Vol. 11101. 11 p. (Lecture Notes in Computer Science).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  Open Access

  File

  • Learning algorithms 100%
  • Population Density 36%
  12 Citations (Scopus)

  199 Downloads (Pure)

• Limits to learning in reinforcement learning hyper-heuristics

  Alanazi, F. & Lehre, K. P., 2016, Evolutionary Computation in Combinatorial Optimization - 16th European Conference, EvoCOP 2016, Proceedings. Springer Verlag, Vol. 9595. p. 170-185 16 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 9595).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Hyper-heuristics 100%
  • Reinforcement Learning 86%
  • Reinforcement learning 57%
  • Learning 52%
  • Heuristics 14%
  12 Citations (Scopus)

• More precise runtime analyses of non-elitist EAs in uncertain environments

  Lehre, P. K. & Qin, X., 26 Jun 2021, GECCO '21: Proceedings of the 2021 Genetic and Evolutionary Computation Conference. Chicano, F. (ed.). New York: Association for Computing Machinery (ACM), p. 1160-1168 9 p. (Genetic and Evolutionary Computation Conference (GECCO)).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Evolutionary algorithms 100%
  • Uncertainty 59%
  • Population Density 49%

• Negative drift in populations

  Lehre, P. K., 2010, Parallel Problem Solving from Nature, PPSN XI - 11th International Conference, Proceedings. PART 1 ed. Vol. 6238 LNCS. p. 244-253 10 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 6238 LNCS, no. PART 1).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Evolutionary algorithms 100%
  • Stochastic Dynamics 65%
  • Evolutionary Algorithms 65%
  • Operator 25%
  • Theorem 22%
  43 Citations (Scopus)

• Non-elitist evolutionary algorithms excel in fitness landscapes with sparse deceptive regions and dense valleys

  Dang, D-C., Eremeev, A. & Lehre, P. K., 26 Jun 2021, GECCO '21: Proceedings of the Genetic and Evolutionary Computation Conference. Chicano, F. (ed.). New York: Association for Computing Machinery (ACM), p. 1133–1141 9 p. (Genetic and Evolutionary Computation Conference (GECCO)).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  Open Access

  File

  • Fitness Landscape 100%
  • Excel 95%
  • Evolutionary Algorithms 83%
  • Sparsity 36%
  • Black Box 19%
  229 Downloads (Pure)

• Non-Elitist Evolutionary Multi-Objective Optimisation: Proof-of-Principle Results

  Liang, Z., Li, M. & Lehre, P. K., 24 Jul 2023, GECCO '23 Companion: Proceedings of the Companion Conference on Genetic and Evolutionary Computation. Association for Computing Machinery (ACM), p. 383-386 4 p. (GECCO: Genetic and Evolutionary Computation Conference).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Evolutionary multiobjective Optimization 100%
  • Multi-objective Evolutionary Algorithm 95%
  • Multiobjective optimization 58%
  • Evolutionary algorithms 56%
  • Update 20%
  1 Citation (Scopus)

• On the limitations of the univariate marginal distribution algorithm to deception and where bivariate EDAs might help

  Lehre, P. K. & Nguyen, P. T. H., 27 Aug 2019, Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic Algorithms (FOGA '19). New York, NY, USA: Association for Computing Machinery (ACM), p. 154-168 15 p.

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  Open Access

  File

  • Population Density 100%
  • Evolutionary algorithms 75%
  • Statistical Models 37%
  9 Citations (Scopus)

  157 Downloads (Pure)

• Populations can be essential in dynamic optimisation

  Dang, D. C., Jansen, T. & Lehre, P. K., 11 Jul 2015, GECCO 2015 - Proceedings of the 2015 Genetic and Evolutionary Computation Conference. Association for Computing Machinery , p. 1407-1414 8 p.

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Dynamic Optimization 100%
  • Evolutionary Algorithms 62%
  • Evolutionary algorithms 48%
  • Evolutionary Optimization 26%
  • Evolutionary Dynamics 23%
  6 Citations (Scopus)

• Refined upper bounds on the expected runtime of non-elitist populations from fitness-levels

  Dang, D. C. & Lehre, P. K., 2014, GECCO 2014 - Proceedings of the 2014 Genetic and Evolutionary Computation Conference. Association for Computing Machinery , p. 1367-1374 8 p.

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Population Density 100%
  • Fitness 92%
  • Population dynamics 60%
  • Upper bound 53%
  • Population Size 34%
  15 Citations (Scopus)

• Runtime analysis of (1+l) EA on computing unique input output sequences

  Lehre, P. & Yao, X., 1 Jan 2007, IEEE Congress on Evolutionary Computation, 2007. CEC 2007.. Institute of Electrical and Electronics Engineers (IEEE), p. 1882-1889 8 p.

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Evolutionary algorithms 100%
  • Testing 24%
  • Finite automata 16%
  17 Citations (Scopus)

• Runtime Analysis of a Co-Evolutionary Algorithm: Overcoming Negative Drift in Maximin-Optimisation

  Hevia Fajardo, M., Lehre, P. K. & Lin, S., 24 Jul 2023, GECCO '23 Companion: Proceedings of the Companion Conference on Genetic and Evolutionary Computation. Association for Computing Machinery (ACM), p. 819–822 4 p. (GECCO: Genetic and Evolutionary Computation Conference).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Evolutionary algorithms 100%

• Runtime Analysis of a Co-Evolutionary Algorithm: Overcoming Negative Drift in Maximin-Optimisation

  Hevia Fajardo, M., Lehre, P. K. & Lin, S., 30 Aug 2023, FOGA '23: Proceedings of the 17th ACM/SIGEVO Conference on Foundations of Genetic Algorithms. Association for Computing Machinery (ACM), p. 73–83 11 p. (FOGA: Foundations of Genetic Algorithms).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  Open Access

  File

  • Evolutionary algorithms 100%
  63 Downloads (Pure)

• Runtime Analysis of Coevolutionary Algorithms on a Class of Symmetric Zero-Sum Games

  Benford, A. & Lehre, P. K., 22 Mar 2024, (Accepted/In press) GECCO '24: Proceedings of the Genetic and Evolutionary Computation Conference. Association for Computing Machinery (ACM), (GECCO: Genetic and Evolutionary Computation Conference).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Game theory 100%

• Runtime analysis of competitive co-evolutionary algorithms for maximin optimisation of a bilinear function

  Lehre, P. K., 8 Jul 2022, GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference. Fieldsend, J. E. (ed.). New York: Association for Computing Machinery (ACM), p. 1408–1416 9 p. (GECCO: Genetic and Evolutionary Computation Conference).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  Open Access

  File

  • Runtime Analysis 100%
  • Maximin 81%
  • Evolutionary Algorithms 75%
  • Evolutionary algorithms 57%
  • Optimization 48%
  103 Downloads (Pure)

• Runtime analysis of selection hyper-heuristics with classical learning mechanisms

  Alanazi, F. & Lehre, P. K., 16 Sept 2014, Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014. Institute of Electrical and Electronics Engineers (IEEE), p. 2515-2523 9 p. 6900602. (IEEE Congress on Evolutionary Computation (CEC); vol. 2014).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Hyper-heuristics 100%
  • Runtime Analysis 91%
  • Learning 52%
  • Heuristics 22%
  • Performance 11%
  22 Citations (Scopus)

• Runtime analysis of the univariate marginal distribution algorithm under low selective pressure and prior noise

  Lehre, P. K. & Nguyen, P. T. H., 13 Jul 2019, The Genetic and Evolutionary Computation Conference 2019 (GECCO 2019). López-Ibáñez, M. (ed.). Association for Computing Machinery (ACM), p. 1497-1505 9 p. (GECCO 2019 - Proceedings of the 2019 Genetic and Evolutionary Computation Conference).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  Open Access

  File

  • Evolutionary algorithms 100%
  3 Citations (Scopus)

  156 Downloads (Pure)

• Self-adaptation Can Help Evolutionary Algorithms Track Dynamic Optima

  Lehre, P. K. & Qin, X., 12 Jul 2023, GECCO ’23: Proceedings of the Genetic and Evolutionary Computation Conference. Association for Computing Machinery (ACM), p. 1619–1627 7 p. (GECCO: Genetic and Evolutionary Computation Conference).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  Open Access

  File

  • Evolutionary algorithms 100%
  • Adaptive algorithms 13%
  23 Downloads (Pure)

• Self-adaptation Can Improve the Noise-tolerance of Evolutionary Algorithms

  Lehre, P. K. & Qin, X., 30 Aug 2023, FOGA '23: Proceedings of the 17th ACM/SIGEVO Conference on Foundations of Genetic Algorithms. Association for Computing Machinery (ACM), p. 105-116 12 p.

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  Open Access

  File

  • Evolutionary algorithms 100%
  • Genomics 22%
  • Adaptive algorithms 21%
  • Experiments 15%
  • Uncertainty 13%
  20 Downloads (Pure)

• Self-adaptation of mutation rates in non-elitist populations

  Dang, D. C. & Lehre, P. K., 31 Aug 2016, PPSN 2016: Parallel Problem Solving from Nature – PPSN XIV . Handl, J., Hart, E., Lewis, P. R., López-Ibáñez, M., Ochoa, G. & Paechter, B. (eds.). Springer Verlag, p. 803-813 11 p. (Lecture Notes in Computer Science (LNCS); vol. 9921 )(Theoretical Computer Science and General Issues (LNTCS); vol. 9921).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  Open Access

  • Self-adaptation 100%
  • Evolutionary Algorithms 70%
  • Mutation 59%
  • Evolutionary algorithms 54%
  • Parameter Tuning 36%
  36 Citations (Scopus)

• Self-adaptation via multi-objectivisation: a theoretical study

  Lehre, P. K. & Qin, X., 8 Jul 2022, GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference. Fieldsend, J. E. (ed.). New York: Association for Computing Machinery (ACM), p. 1417-1425 9 p. (GECCO: Genetic and Evolutionary Computation Conference).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  Open Access

  File

  • Self-adaptation 100%
  • Mutation 59%
  • Chromosomes 34%
  • Multiobjective optimization 27%
  • Fitness 22%
  81 Downloads (Pure)

• Self-adaptation via multi-objectivisation: an empirical study

  Qin, X. & Lehre, P. K., 14 Aug 2022, Parallel Problem Solving from Nature – PPSN XVII: 17th International Conference, PPSN 2022, Dortmund, Germany, September 10–14, 2022, Proceedings, Part I. Rudolph, G., Kononova, A. V., Aguirre, H., Kerschke, P., Ochoa, G. & Tušar, T. (eds.). 1 ed. Springer, p. 308–323 16 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 13398 LNCS).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  Open Access

  File

  • Self-adaptation 100%
  • Empirical Study 67%
  • Evolutionary Algorithms 60%
  • Evolutionary algorithms 54%
  • Mutation 50%
  52 Downloads (Pure)

• Simplified runtime analysis of estimation of distribution algorithms

  Dang, D. C. & Lehre, P. K., 11 Jul 2015, GECCO 2015 - Proceedings of the 2015 Genetic and Evolutionary Computation Conference. Association for Computing Machinery , p. 513-518 6 p.

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Estimation of Distribution Algorithms 100%
  • Runtime Analysis 99%
  • Marginal Distribution 64%
  • Univariate 55%
  • Stochastic Search 43%
  30 Citations (Scopus)

• The generalized minimum spanning tree problem: A parameterized complexity analysis of Bi-level optimisation

  Corus, D., Lehre, P. K. & Neumann, F., 2013, GECCO 2013 - Proceedings of the 2013 Genetic and Evolutionary Computation Conference. p. 519-525 7 p.

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Bilevel Optimization 100%
  • Complexity Analysis 85%
  • Minimum Spanning Tree 83%
  • Parameterized Complexity 79%
  • Systems Analysis 76%
  6 Citations (Scopus)

• Unbiased black-box complexity of parallel search

  Badkobeh, G., Lehre, P. K. & Sudholt, D., 24 Sept 2014, Parallel Problem Solving from Nature – PPSN XIII: 13th International Conference Ljubljana, Slovenia, September 13-17, 2014 Proceedings. Bartz-Beielstein, T., Branke, J., Filipic, B. & Smith, J. (eds.). 1 ed. Springer, p. 892-901 10 p. (Lecture Notes in Computer Science ; vol. 8672).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Black Box 100%
  • Mutation 35%
  • Lower bound 34%
  • Function evaluation 33%
  • Evolutionary algorithms 26%
  41 Citations (Scopus)