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Computer Science

D-Index
33
Citations
3616
World Ranking
12801
National Ranking
814

Overview

Per Kristian Lehre is affiliated with the University of Birmingham in the United Kingdom. Their research primarily focuses on areas within Computer Science, particularly Artificial Intelligence and Computational Theory and Mathematics. Lehre's work spans multiple subfields, including Management Science and Operations Research, Computer Networks and Communications, and Genetics.

Lehre's recent publication record illustrates a concentration on evolutionary algorithms and optimization techniques. Notable papers include:

  • Tail bounds on hitting times of randomized search heuristics using variable drift analysis, 2020, Combinatorics Probability Computing
  • Runtime analysis of competitive co-evolutionary algorithms for maximin optimisation of a bilinear function, 2022, Proceedings of the Genetic and Evolutionary Computation Conference

Frequent venues where Lehre publishes include the Proceedings of the Genetic and Evolutionary Computation Conference, where they have contributed extensively, as well as Algorithmica, arXiv (Cornell University), the Proceedings of the Genetic and Evolutionary Computation Conference Companion, and the Proceedings of the AAAI Conference on Artificial Intelligence.

The scientist's collaborations reflect ongoing partnerships with several researchers. Frequent co-authors include Xiaoyu Qin, Mario Alejandro Hevia Fajardo, Shishen Lin, Duc-Cuong Dang, and Anton V. Eremeev.

Research topics covered by Lehre include:

  • Metaheuristic Optimization Algorithms Research
  • Evolutionary Algorithms and Applications
  • Advanced Multi-Objective Optimization Algorithms
  • Reinforcement Learning in Robotics
  • Artificial Intelligence in Games
  • Game Theory and Applications
  • Optimization and Search Problems

In addition to the papers where Lehre is lead or sole author, they are associated with several highly cited works co-authored by Duc-Cuong Dang, such as:

  • Escaping Local Optima with Non-Elitist Evolutionary Algorithms, 2021, Proceedings of the AAAI Conference on Artificial Intelligence
  • Non-elitist evolutionary algorithms excel in fitness landscapes with sparse deceptive regions and dense valleys, 2021, Proceedings of the Genetic and Evolutionary Computation Conference
  • Fast non-elitist evolutionary algorithms with power-law ranking selection, 2022, Proceedings of the Genetic and Evolutionary Computation Conference

Best Publications

  • Black-Box Search by Unbiased Variation

    Per Kristian Lehre;Carsten Witt

  • Black-box search by unbiased variation

    Per Kristian Lehre;Carsten Witt

  • Escaping Local Optima Using Crossover With Emergent Diversity

    Duc-Cuong Dang;Tobias Friedrich;Timo Kotzing;Martin S. Krejca

  • Level-Based Analysis of Genetic Algorithms and Other Search Processes

    Dogan Corus;Duc-Cuong Dang;Anton V. Eremeev;Per Kristian Lehre

  • Crossover can be constructive when computing unique input–output sequences

    Per Kristian Lehre;Xin Yao

  • Fitness-levels for non-elitist populations

    Per Kristian Lehre

  • Negative drift in populations

    Per Kristian Lehre

  • On the Impact of Mutation-Selection Balance on the Runtime of Evolutionary Algorithms

    P. K. Lehre;Xin Yao

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

    Philipp Rohlfshagen;Per Kristian Lehre;Xin Yao

  • Self-adaptation of Mutation Rates in Non-elitist Populations

    Duc-Cuong Dang;Per Kristian Lehre

  • Unbiased Black-Box Complexity of Parallel Search

    Golnaz Badkobeh;Per Kristian Lehre;Dirk Sudholt

  • Runtime Analysis of Non-elitist Populations: From Classical Optimisation to Partial Information

    Duc-Cuong Dang;Per Kristian Lehre

  • On the impact of the mutation-selection balance on the runtime of evolutionary algorithms

    Per Kristian Lehre;Xin Yao

  • Escaping Local Optima with Diversity Mechanisms and Crossover

    Duc-Cuong Dang;Tobias Friedrich;Timo Kötzing;Martin S. Krejca

  • Faster black-box algorithms through higher arity operators

    Benjamin Doerr;Daniel Johannsen;Timo Kötzing;Per Kristian Lehre

  • Concentrated Hitting Times of Randomized Search Heuristics with Variable Drift

    Per Kristian Lehre;Carsten Witt

  • On the effect of populations in evolutionary multi-objective optimization

    Oliver Giel;Per Kristian Lehre

  • On the effect of populations in evolutionary multi-objective optimisation**

    Oliver Giel;Per Kristian Lehre

  • Crossover Can Be Constructive When Computing Unique Input Output Sequences

    Per Kristian Lehre;Xin Yao

  • When is an estimation of distribution algorithm better than an evolutionary algorithm

    Tianshi Chen;Per Kristian Lehre;Ke Tang;Xin Yao

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

    Per Kristian Lehre;Ender Özcan

  • Simplified Runtime Analysis of Estimation of Distribution Algorithms

    Duc-Cuong Dang;Per Kristian Lehre

Frequent Co-Authors

Xin Yao
Xin Yao Lingnan University
Dirk Sudholt
Dirk Sudholt University of Sheffield
Frank Neumann
Frank Neumann University of Adelaide
Carsten Witt
Carsten Witt Technical University of Denmark
Benjamin Doerr
Benjamin Doerr École Polytechnique
Tobias Friedrich
Tobias Friedrich Hasso Plattner Institute
Julian F. Miller
Julian F. Miller University of York
Thomas Jansen
Thomas Jansen Aberystwyth University
Mike Preuss
Mike Preuss Leiden University
Carlos M. Fonseca
Carlos M. Fonseca University of Coimbra

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