SIGEVO Dissertation Award

The SIGEVO Best Dissertation award will recognize excellent thesis research by doctoral candidates in the field of evolutionary computing. Dissertations will be reviewed for technical depth and significance of the research contribution, potential impact on the field of evolutionary computing, and quality of presentation. The SIGEVO Best Dissertation award will be given annually to a maximum of 1 winner and a maximum of 2 honorable mentions. The award presentation will take place at the Genetic and Evolutionary Computation Conference (GECCO) awards ceremony. The award will carry a monetary value, contributed by SIGEVO, of $2,000 to be awarded to the winner and $1,000 to each of the honorable mentions.  The award winner and honorable mentions will each receive a beautiful plaque.


Eligible dissertations must have been successfully defended and deposited in the previous calendar year (January to December).

Nominees do not need to be SIGEVO/ACM members.

Nominations are welcome from any country, but only English language versions will be accepted.

Each nominated dissertation must be on any topic relevant to evolutionary computing. The determination of whether a thesis is within the scope of the award will be made by the SIGEVO Dissertation Award Committee. A dissertation can be nominated for both the SIGEVO Dissertation Award and the ACM Doctoral Dissertation Award.

Submission Deadline

March 1, 2024

Decision Deadline

The SIGEVO Dissertation Award Committee will make a recommendation to the Chair of SIGEVO by May 1 of each year. Upon approval of the recommendation by the executive board of ACM SIGEVO, the chair will inform the winner and honorable mentions, if any.

Submission Procedure

All nomination materials must be submitted electronically to the current chair of the SIGEVO Dissertation Award Committee (Bing Xue <Bing.Xue@ecs.vuw.ac.nz>, ask for confirmation) by the submission deadline, in English. PDF format is preferred for all materials. Late submissions will not be considered.

Nomination for the award must include:

  1. An English language copy of thesis, in legible pdf format.
  2. A statement from the advisor(s), limited to 2 pages, addressing why the nominee’s dissertation should receive this award.  This should discuss the significance of the dissertation in depth. The nomination must come from the advisor(s); self-nomination is not allowed.
  3. Three (3) letters of support (in addition to the nomination letter) limited to a maximum of 2 pages each. Supporting letters should be included from other experts in the field who can provide additional insights or evidence of the dissertation’s impact. If a letter writer is supporting more than one nomination in a year, they may be asked to rank those nominations. At least two letters must come from experts outside the nominee’s university.
  4. A list of publications by the nominee.
  5. Suggested citation: This should be a concise statement (maximum of 50 words) describing the key technical contribution for which the candidate merits this award. Final wording for awardees will be at the discretion of the SIGEVO Dissertation Award Committee.

Frequently Asked Questions

  1. Q: Should the letters of recommendation be sent directly to the current chair of the SIGEVO Dissertation Award Committee or should they be sent to the advisor to be included in the nomination package?
    A: They should be sent to the advisor.
  2. Q: Should the list of publications contain only the publications related to the dissertation?
    A: No, they should be a full list of publications.
  3. Q: Does the committee also accept cumulative PhD theses (also called Journal-format), which are not a monograph but a collection of papers on a specific topic?''
    A: Yes, as long as the submitted work is a cohesive well-written document with an overall introduction and conclusion. The work submitted must have been examined as a valid submission to a PhD degree.

2023 SIGEVO Dissertation Award

  • Winner: ''Automatic Algorithm Configuration: Methods and Applications" by Marcelo De Souza
    This dissertation takes a deep dive on the topic of automatic algorithm configuration, with a particular focus on heuristic and evolutionary algorithms. It presents novel methods to improve the automatic configuration of algorithms, producing better results, reducing the computational effort, and facilitating its analysis and understanding. It also proposes a solver that applies such techniques to automatically design heuristic and evolutionary algorithms for a broad class of binary optimization problems.
  • Honorable Mention: ''Algorithm Configuration Landscapes: Analysis and Exploitation" by Yasha Pushak
    ''This dissertation challenges long-held beliefs in and brings attention to a mostly overlooked research area, namely search landscape analysis for automated algorithm configuration (AAC) and AutoML. It shows that these landscapes have a simple structure and contain patterns that can be exploited to improve the state of the art in automated algorithm configuration. This result is demonstrated on various applications of AAC."
  • Honorable Mention: ''Enhancing Evolutionary Algorithm Performance with Knowledge Transfer and Asynchronous Parallelism" by Eric O. Scott
    ''This dissertation advances the theory of evolutionary knowledge transfer and demonstrates the effectiveness of asynchronous evolutionary algorithms. It provides guidelines on how to design asynchronous evolutionary algorithms, proves no-free lunch theorems and runtime results for evolutionary knowledge transfer methods and considers representation-based versions of evolutionary knowledge transfer. Thus, it perfectly combines rigorous theoretical research with empirical analysis. The techniques proposed here solve important problems in scaling optimization to large compute clusters and generalizing intelligently to new tasks. The resulting software is widely used and has been tested on one of the world's largest supercomputers."
  • SIGEvolution article
  • Selection committee:
    • Francisco Chicano, University of Málaga, Spain
    • Jonathan Fieldsend, University of Exeter, UK
    • Ting Hu, Queen's University, Canada
    • Manuel López-Ibáñez, University of Málaga, Spain
    • Tea Tušar, Jožef Stefan Institute, Slovenia
    • Christine Zarges, Aberystwyth University, UK

2022 SIGEVO Dissertation Award

  • Winner: ''Automated Software Transplantation" by Alexandru Marginean
    This dissertation proposes tools for multi-lingual automated code transplantation based on evolutionary computation to transplant functionality from one piece of software to another one. These methods speed up software development and automate bug fixing. Already 2.9 billion people daily run software that has been automatically repaired by the Sapfix system described in this work.
  • Honorable Mention: ''Discovering the Preference Hypervolume: an Interactive Model for Real World Computational Co-creativity" by Alexander Hagg
    ''This dissertation transcends the usual domain-oriented research by connecting cognitive psychology with evolutionary computation and machine learning. It develops a novel co-creative process, which considerably improves the existing Quality Diversity (QD) methods, for supporting engineers to efficiently discover many innovative solutions in early design stages, with a demonstration of its practical application in the domains of fluid dynamics, the built environment and robotics."
  • Honorable Mention: Genetic Programming Hyper-heuristics for Dynamic Flexible Job Shop Scheduling by Fangfang Zhang
    "This dissertation proposes multiple innovative ideas for developing new hyper-heuristics based on genetic programming that tackle challenging dynamic-flexible production scheduling problems. It develops novel surrogate models, genetic operators, feature selection algorithms and multi-task techniques, significantly improving training efficiency, and the accuracy and interpretability of evolved scheduling rules."
  • SIGEvolution article
  • Selection committee:
    • Francisco Chicano, University of Málaga, Spain
    • Kenneth De Jong, George Mason University, USA
    • Jonathan Fieldsend, University of Exeter, UK
    • Manuel López-Ibáñez, University of Málaga, Spain
    • Tea Tušar, Jožef Stefan Institute, Slovenia
    • Christine Zarges, Aberystwyth University, UK

2021 SIGEVO Dissertation Award

2020 SIGEVO Dissertation Award

  • Winner: Evolving Principles of Artificial Neural Design by Dennis Wilson
  • Honorable Mention: Theoretical Analyses of Univariate Estimation-of-Distribution Algorithms by Martin Stefan Krejca
  • Honorable Mention: Multi-Objective Mixed-Integer Evolutionary Algorithms for Building Spatial Design by Koen van der Blom
  • SIGEvolution article
  • Selection committee:
    • Anne Auger, Inria, CMAP Ecole Polytechnique, IP Paris, France
    • Francisco Chicano, University of Málaga, Spain
    • Kenneth De Jong, George Mason University, USA
    • Jonathan Fieldsend, University of Exeter, UK
    • Manuel López-Ibáñez, University of Málaga, Spain
    • Gabriela Ochoa, University of Stirling, UK