Volume 16, Issue 4

newsletter of the ACM Special Interest Group on Genetic and Evolutionary Computation


Welcome to the last 2023 (Winter) issue of the SIGEvolution newsletter! We start by celebrating this year’s ACM SIGEVO Outstanding Contribution Awardees: Prof. Emma Hart and Dr. Carlos A. Coello Coello, who kindly shared insights on their contributions and perspectives on evolutionary computation. We follow with an overview and highlights of this year’s GECCO (the Genetic and Evolutionary Computation Conference) celebrated in the charming city of Lisbon, the westernmost capital in mainland Europe (see cover image). Sara Silva (General Chair) and Luís Paquete (EiC) provided an informative and visual account of a wonderful and memorable event. We conclude with the usual announcements, forthcoming events and call for papers. Remember to get in touch if you’d like to contribute or have suggestions for future issues of the newsletter.

Gabriela Ochoa (Editor)

The 2023 ACM SIGEVO Outstanding Contribution Awardees

The SIGEVO Outstanding Contribution Award recognizes remarkable contributions to Evolutionary Computation (EC) when evaluated over a sustained period of at least 15 years. These contributions can include technical innovations, publications, leadership, teaching, mentoring, and service to the EC community.

In 2023, two amazing members of our community received this recognition: Prof. Emma Hart, and Dr. Carlos A. Coello Coello. To celebrate these distinctions, Emma and Carlos kindly answered our questions reflecting on their contributions and views on evolutionary computation as well as their valuable advice to young researchers in the field.

Prof. Emma Hart

Napier Edinburgh University, Edinburgh, Scotland, UK

Q1: Which are the service, editorial, leadership, mentoring or other contributions you are more proud of?

I am really proud of being EiC of ECJ for seven years. It was an honour to have the community-put their trust in me in to lead it, to be the first female EiC and to be able to engage with so many people from the community, and to see its impact factor grow. I was particularly pleased with my last issue as Editor celebrating ECJ’s 30 year anniversary, where I invited authors who published in the very first issue to write articles. I really enjoyed engaging with all the contributors and was fascinating to see the different perspectives 30 years on.

Q2: Which are your most significant technical contributions to Evolutionary Computation (EC)?

I was privileged to be involved in the early days of the field of hyper-heuristics [1] which is still going strong. I am also proud of some of the more recent work around building systems that learn – in evolutionary robotics, and in continually improving optimisers [2] and feature-free selection [3]

Q3: What are the current open problems, or topics where you think there are opportunities for substantial contributions in our field?

There is a huge opportunity for research that combines EC and ML – we are already seeing this for example in the new tracks at GECCO on ML for Evo and Evo for ML. In particular, I think we need to start to think about systems that continually learn. This could mean developing optimisation algorithms that improve over time based on past experience, using knowledge extracted every time an algorithm is run. Optimisers also need to be adaptive in response to changing characteristics of the instances they solve over time so their performance doesn’t degrade. Despite all the hype, I’m also excited by some of the opportunities for EC methods in generative AI, particularly in design applications, for example robotics.

Q4: Do you think the current AI hype is an opportunity or a threat for EC?

Probably a bit of both. The general hype can put people off but I think in EC we have been doing ‘generative AI’ for a long time, for example using EC for design (robots, objects) and in art – we just didn’t use the label. I think EC can contribute a lot to the current interest in generative design, and we are already seeing that at GECCO.

Q5: How do you view the visibility of the EC community in the larger computer science community?

It’s still not ‘mainstream’ in CS but actually I see it gaining interesting in a lot of other fields – for example I’m currently working with engineering colleagues on using EC to design earthquake protection barriers and another group in designing buildings – these communities are very excited about it!]

Q6: What advice do you have for the younger generation of researchers in the field?

Meet people! Talk to people at conferences, both your peers and more senior people, and take opportunities to volunteer for things. So many of my collaborations (and friends!) have come from gradually building up a network of people from all across the world. Also don’t be afraid to try out new ideas or to gradually shift to new topics.


[1] Burke, E., Kendall, G., Newall, J., Hart, E., Ross, P., Schulenburg, S. (2003). Hyper-Heuristics: An Emerging Direction in Modern Search Technology. In: Glover, F., Kochenberger, G.A. (eds) Handbook of Metaheuristics. International Series in Operations Research & Management Science, vol 57. Springer, Boston, MA. https://doi.org/10.1007/0-306-48056-5_16

[2] Kevin Sim, Emma Hart, Ben Paechter; A Lifelong Learning Hyper-heuristic Method for Bin Packing. Evol Comput 2015; 23 (1): 37–67. doi: https://doi.org/10.1162/EVCO_a_00121

[3] Alissa, M., Sim, K. & Hart, E. Automated Algorithm Selection: from Feature-Based to Feature-Free Approaches. J Heuristics 29, 1–38 (2023). https://doi.org/10.1007/s10732-022-09505-4

Dr. Carlos A. Coello Coello

Departamento de Computación, CINVESTAV-IPN, Mexico DF, Mexico

Q1: Which are the service, editorial, leadership, mentoring or other contributions you are more proud of?

I like all these activities, but mentoring students is certainly the one I’m most proud of. I think that educating young people and preparing new generations of computer scientists is exciting and stimulating. I have learned a lot from my students, and I always pay attention to their ideas. I also like to challenge them so that they can discover that they can achieve much more of what they think. Particularly, I can say that witnessing the transformation of a student into a scientist is a very unique experience which I enjoy a lot.
The contributions made with my students (which are more their contributions than mine) are the ones I feel most proud of. And, I should also add that I feel very proud of them.

Q2: Which are your most significant technical contributions to Evolutionary Computation (EC)?

My main research area has been the design of multi-objective evolutionary algorithms. I was lucky enough to be an early researcher in an area which is called today “evolutionary multi-objective optimization” (EMO) and this allowed me to make a few contributions that have been considered relevant by my peers. For example, in my research group, we designed the first micro genetic algorithm (with a population of only 4 individuals) for multi-objective optimization [1]. We also proposed the first Pareto-based multi-objective artificial immune system [2] and one of the earliest Pareto-based multi-objective particle swarm optimizers [3]

Additionally, we have made relevant contributions to the development constraint-handling techniques that adopt multi-objective concepts [4] and provided the first proof of convergence of a multi-objective artificial immune system [5].

More recently, we also made relevant contributions to large-scale multi-objective optimization [6] and to the identification of the real sources of difficulty of problems having many (i.e., more than three) objectives [7].

Q3: What are the current open problems, or topics where you think there are opportunities for substantial contributions in our field?

Focusing on evolutionary multi-objective optimization, I believe that there are many open problems that are of great importance. For example, how to deal with very expensive objective functions in an effective and efficient way. Scalability (both in decision variable space and in objective function space) is another topic which remains relevant despite the significant amount of research that has been conducted in the last few years. The reason for that is that it still has many issues that have not been properly addressed. For example, we need to analyze the sort of diversity that we should expect in a very high-dimensional objective space if our multi-objective evolutionary algorithms use relatively small population sizes (of a few hundreds of individuals). Also, it is necessary to conduct more research on performance indicators, since we currently know only one which is fully Pareto compliant (the hypervolume, which is very costly when dealing with more than six objectives).
Other topics that I consider relevant are: parallel multi-objective evolutionary algorithms (research in this area is still very scarce), the potential use of coevolution in certain types of multi-objective problems, the proper incorporation of machine learning techniques that aim to deal in a more efficient way with problems that have a high degree of complexity (e.g., because of their high dimensionality). Finally, the theoretical foundations of several aspects of evolutionary multi-objective optimization (e.g., run-time analysis) is also a pending task.

Q4: Do you think the current AI hype is an opportunity or a threat for EC?

It’s an opportunity. Most of the AI hype has been caused by the use of deep neural networks, which are a computational intelligence technique. Let’s keep in mind that evolutionary computation is also a computational intelligence technique, and we can certainly benefit of this. Now that many people are paying attention to neural networks, we can try to get them interested in evolutionary computation as well. Evolutionary computation offers techniques that can nicely complement the use of neural networks. In fact, in recent years, there has been an increased interest in evolutionary machine learning (e.g., using evolutionary algorithms to evolve neural network architectures). However, I envision many more interactions between evolutionary algorithms and neural networks. For example, I believe that it’s worth exploring the use of hybrids between evolutionary algorithms and reinforcement learning and that it’s worth developing lightweight (in terms of computational cost) machine learning techniques that can help evolutionary algorithms to perform a more “informed” search so that they can be more efficient and effective while consuming less computational resources.

Q5: How do you view the visibility of the EC community in the larger computer science community?

It has increased, but unfortunately, it’s still relatively low. Today, most textbooks on artificial intelligence include evolutionary algorithms, but the presence of evolutionary computation in the top AI conferences is still rather limited and it mainly includes work focused on evolutionary machine learning. Something interesting is that evolutionary computation has gained more visibility within optimization (many textbooks on optimization now include evolutionary algorithms as a direct search method). However, an important portion of the traditional computer science community still pays very limited attention to evolutionary computation.

Q6: What advice do you have for the younger generation of researchers in the field?

My main advice for them is that they should try to be disruptive. Today, much of the research conducted in some areas (e.g., in evolutionary multi-objective optimization) consists of replacing some component of a well-known algorithm by another one that marginally improves its performance. In most cases, there is little justification for these proposals and the changes also reflect little novelty. It’s necessary to have fresh ideas, even if they are radical and controversial. That’s the only way in which a research area may remain active and in constant evolution.


[1] Coello Coello, C.A., Toscano Pulido, G. (2001). A Micro-Genetic Algorithm for Multiobjective Optimization. In: Zitzler, E., Thiele, L., Deb, K., Coello Coello, C.A., Corne, D. (eds) Evolutionary Multi-Criterion Optimization. EMO 2001. Lecture Notes in Computer Science, vol 1993. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44719-9_9
[2] Coello, C.A.C., Cortés, N.C. Solving Multiobjective Optimization Problems Using an Artificial Immune System. Genet Program Evolvable Mach 6, 163–190 (2005). https://doi.org/10.1007/s10710-005-6164-x
[3] C. A. C. Coello, G. T. Pulido and M. S. Lechuga, “Handling multiple objectives with particle swarm optimization,” in IEEE Transactions on Evolutionary Computation, vol. 8, no. 3, pp. 256-279, June 2004, doi: 10.1109/TEVC.2004.826067. https://ieeexplore.ieee.org/document/1304847
[4] Carlos A. Coello Coello (2000) Constraint-Handling using an Evolutionary Multiobjective Optimization Technique, Civil Engineering and Environmental Systems, 17:4, 319-346, DOI: 10.1080/02630250008970288 https://www.tandfonline.com/doi/abs/10.1080/02630250008970288
[5] Villalobos-Arias, M., Coello, C.A.C., Hernández-Lerma, O. (2004). Convergence Analysis of a Multiobjective Artificial Immune System Algorithm. In: Artificial Immune Systems. ICARIS 2004. Lecture Notes in Computer Science, vol 3239. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30220-9_19
[6] L. M. Antonio and C. A. C. Coello, “Use of cooperative coevolution for solving large scale multiobjective optimization problems,” 2013 IEEE Congress on Evolutionary Computation, Cancun, Mexico, 2013, pp. 2758-2765, doi: 10.1109/CEC.2013.6557903. https://ieeexplore.ieee.org/document/6557903
[7] O. Schutze, A. Lara and C. A. C. Coello, “On the Influence of the Number of Objectives on the Hardness of a Multiobjective Optimization Problem,” in IEEE Transactions on Evolutionary Computation, vol. 15, no. 4, pp. 444-455, Aug. 2011, doi: 10.1109/TEVC.2010.2064321. https://ieeexplore.ieee.org/document/5601759

Overview of GECCO 2023

By Sara Silva and Luís Paquete

The 2023 Genetic and Evolutionary Computation Conference (GECCO 2023) took place in Lisbon, Portugal, on July 15th-19th, in hybrid mode. This was the second fully hybrid GECCO, where both speakers and non-speakers were able to participate either onsite or online. In the following, we provide statistics about submissions and authorship, as well as the evolution and growth of GECCO since 2005.

GECCO 2023 would not have been possible without an enormous team of dedicated people: 37 organisers, supported by the business committee; 8 members of the local team, together with 24 local volunteers and 55 international student volunteers; the chairs of the 13 tracks, the 24 workshops, including the student workshop, and the 12 competitions. We also thank all our speakers, including the 3 keynotes (Riccardo Poli, Carla Gomes, Kenneth De Jong) and the presenters of the 32 tutorials; all the reviewers; the sponsors and supporters; the chairs of the summer school that took place two days before the conference; and many more. We thank everyone for contributing to the efforts of this community event.

GECCO 2023 was organized into these 13 main tracks: Ant Colony Optimization and Swarm Intelligence (ACO-SI), Complex Systems (CS), Evolutionary Combinatorial Optimization and Metaheuristics (ECOM), Evolutionary Machine Learning (EML), Evolutionary Multiobjective Optimization (EMO), Evolutionary Numerical Optimization (ENUM), Genetic Algorithms (GA), General Evolutionary Computation and Hybrids (GECH), Genetic Programming (GP), Neuroevolution (NE), Real World Applications (RWA), Search-Based Software Engineering (SBSE) and Theory.

Among all the tracks, GECCO 2023 received 519 full paper submissions and accepted 180 of them, which resulted in a 34.7% acceptance rate. GECCO 2023 also received 55 poster paper submissions. The total number of accepted posters was 187, of which 148 from full paper submissions and 39 from poster paper submissions. Among the accepted full papers, 26 were nominated for the Best Paper Award. For the first time at GECCO, we have acknowledged those reviewers that excelled over their peers, awarding them the Outstanding Reviewer certificate.

Figure 1. GECCO 2023 full paper submissions and acceptance rates per track.

Figure 1 shows the number of submissions (light blue), the number of accepted full papers (yellow), the acceptance rate of each track (solid blue line) and the acceptance rate of the conference (dotted blue line). The tracks are sorted by the number of full paper submissions they received (decreasing from left to right). The EML track received the largest number of submissions. The acceptance rate was different in each track, with the largest deviations from the average being observed in the Theory and ACO-SI tracks (55% and 42%, respectively) and in the GECH and ENUM tracks (20% and 27%, respectively).

Figure 2. List of countries covering the top 80% of GECCO 2023 submitting authors.

The submissions received by the main tracks at GECCO 2023 were authored by 1460 researchers from 57 countries, with 80% of these authors affiliated with the 16 countries shown in Figure 2 (where the top axis shows the number of submissions). The USA was, once again, the country with most authors (170); also in the top 5 were Germany (147), China (137), United Kingdom (121), and France (103).

Besides the papers and posters of the main conference, GECCO 2023 also accepted 119 workshop papers (12 of which in the student workshop), 32 tutorials, 18 Late-Breaking Abstracts and 29 Hot-Off-the-Press contributions. The Humies event presented its 8 finalists, this year in a plenary session. These and other events were packed in four and a half amazing days of scientific and social program!

Figure 3. Full paper submissions and acceptance rates for past GECCO editions.

Figure 3 compares the number of submissions and acceptance rate of GECCO 2023 to its previous editions. With 21.5% more submissions than in the previous year, GECCO@Lisbon definitely marked a return to pre-pandemic numbers, establishing a new record for the past 9 years by surpassing other European cities like Madrid, Berlin and Prague, and even the 2018 Kyoto edition. The acceptance rate was one of the lowest ever, matching the record low of the past 9 years (35%) that was set by the 2019 Prague edition; only once the acceptance rate was lower than this, in 2014 in Vancouver (33%).

Regarding the number of participants, it is clear that the hybrid mode is a real success, allowing non-travelling participants to have a remote presence in every scientific session of the program. Although not reaching the record number of registrations of the previous year in Boston (987, around half of them online), GECCO 2023 counted 883 registrants, of which 272 (around one-third) were online and 611 onsite. Around 17% of the papers and 25% of the posters were presented remotely.

Like the previous edition in Boston, GECCO@Lisbon relied on established tools to provide networking and socialization among the online attendees and between the online and onsite participants. Gather and Whova played that role. Gather was mostly used for the poster sessions and coffee breaks, while Whova was used for all the other sessions. All attendees can still watch the pre-recorded videos of the talks, available through the Whova agenda.

There is another important aspect of running GECCO in hybrid mode. Not only it makes a more inclusive conference, it also increases the sustainability of the event. In hybrid GECCO, participants may choose not to travel, therefore reducing their environmental footprint. For the ones who did travel to Lisbon, we also made several efforts to increase the sustainability of their participation. For meals and drinks, glass and paper were preferred instead of plastic; the bag and cap that were offered at registration were made from cotton (organic for the cap), as well as the lanyards for the badges (Figure 4). Although we did print a number of copies of the program (for the participants who prefer a non-electronic agenda), it was a very condensed version of the full booklet (without any plastic binding). With our eyes on the future, we did a first trial with paper badges only for the summer school, which unfortunately revealed to be less resistant than needed for the entire conference duration. While we wait and hope for more sustainable and resistant materials in the future, this year we asked all participants to return their plastic badges at the end of the conference, to be recycled and/or reused. A rechargeable (plastic-free) transportation card was also offered at registration, to incentivize the use of the subway; participants were asked to fill out a mobility survey with simple questions regarding long-distance and local travel. With all the information gathered, we will be publishing a full report on the sustainability of GECCO 2023, in a following issue.

Figure 4. Bag and cap given at registration, made from cotton (organic for the cap); lanyard for the badge, also in cotton; rechargeable (plastic-free) transportation card.

The entire organization team (some of us in Figure 5) thanks all the participants, onsite and online. We did our best to provide you with a fantastic conference, and it warms our hearts that the final GECCO survey earned us nothing less than 4.6 stars! We appreciate all your comments and suggestions for improvement. GECCO would not exist without you!

Figure 5. From left to right: Luís Paquete, Editor-in-Chief; Sara Silva, General Chair; Leonardo Vanneschi, Local Chair; Aleš Zamuda and Nuno Lourenço, Hybridization Chairs.


motipy: the Metaheuristic Optimization in Python Library

By Prof. Dr. Thomas Weise, Hefei University, Hefei, Anhui, China

We are thankful for the opportunity to announce our Python package “moptipy,” which offers a rich set of tools for implementing, applying, and experimenting with metaheuristic optimization algorithms. Our package has been designed with benchmarking in mind and thus provides very comprehensive experimentation, data collection, and evaluation facilities out of the box. moptipy has the following features:

  1. Very comprehensive documentation with many examples, reaching down to literature references inside the code and up to complex example experiments.
  2. Several standard search spaces (bit strings, permutations, real vectors), operators, and algorithms, including randomized local search/hill climbers, simulated annealing, evolutionary algorithms, memetic algorithms, NSGA-II, several numerical optimization algorithms, etc., are already implemented and ready for use.
  3. It is very easy to implement new algorithms using moptipy, be they general black-box methods or tailored for specific optimization problems.
  4. It is also easy to integrate algorithms from external libraries and unify them under our API, which we did as proof-of-concept with CMA-ESBOBYQA, as well as for the algorithms from SciPy.
  5. Data can be collected at different verbosity levels, ranging from only providing the final result and its quality via the API (without creating any files) to creating log files with all (or all improving) steps of an algorithm, the result, the algorithm and problem parameters, the system setup, non-dominated solutions, and the random seed for fully-reproducible experiments. [1]
  6. An experiment execution facility for very simple and robust parallel and distributed experimentation is provided. Parallelization and distribution works out-of-the-box based on shared folders and thus does not require additional libraries or programming effort [1].
  7. Stopping criteria for optimization processes can be defined based on goal solution qualities, clock time, and/or consumed objective function evaluations.
  8. All experiments are fully reproducible, i.e., from a log file an algorithm and problem can be configured such that, in the replication experiment, exactly the same search steps are performed as in the original setting [1].
  9. The experiment evaluation facility can parse the log files and generate progress plots, result tables, ERT and ECDF plots, statistical test tables, and export data towards Excel or the popular IOHanalyzer.
  10. Both single-objective and multi-objective optimization are supported under the same unified API.
  11. The package has a good unit test coverage and pre-defined tools to unit test your own code. When implementing new objective functions, algorithms, encodings, oder operators, it is possible to use these tools to look for errors.

The package is written in Python (>= 3.10), which currently probably is the predominant language in machine learning and AI as well as maybe the most-often used language in university classes. moptipy is therefore ideal for the use by both students and practitioners in AI, ML, or computer science in general. It can be installed from PyPI simply by doing

pip install moptipy

After the installation, the many examples listed at https://thomasweise.github.io/moptipy/#examples can be executed to get familiar with the package.

Some of the design rationales behind moptipy are discussed in

[1]. Thomas Weise and Zhize Wu. Replicable Self-Documenting Experiments with Arbitrary Search Spaces and Algorithms. In Sara Silva and Luís Paquete, eds., Companion Proceedings of the Conference on Genetic and Evolutionary Computation (GECCO’23), Lisbon, Portugal, July 15-19, 2023, pages 1891-1899. ACM. 2023. doi:10.1145/3583133.3596306.


New Tracks at GECCO 2024

By Julia Handl (EiC) and Xiadong Li (General Chair)

A short heads-up from the GECCO 2024 organizers: in addition to the 13 well-established GECCO tracks, we are introducing two new tracks this year:

L4EC – Learning for Evolutionary Computation, Track Chairs Pascal Kerschke & Marie-Eléonore Kessaci
BBSR – Benchmarking, Benchmarks, Software, and Reproducibility, Track Chairs Carola Doerr & Arnaud Liefooghe
Details for these and all other GECCO tracks: https://gecco-2024.sigevo.org/Tracks

Please support GECCO 2024, and these new tracks, by submitting your best results. Don’t hesitate to get in touch with the track chairs in case of questions.

We are looking forward to seeing you in Melbourne!

ACM TELO Special Issue on Evolutionary Reinforcement Learning

Journal Editors: Juergen Branke, Manuel López-Ibáñez

Special Issue Editors: Adam Gaier, Giuseppe Paolo, Antoine Cully

The latest issue of the ACM Transactions on Evolutionary Learning and Optimization (TELO), Volume 3, Issue 3, is a special issue on “Evolutionary Reinforcement Learning”, the table of contents is:

  • Editorial to the “Evolutionary Reinforcement Learning” Special Issue
    Adam Gaier, Giuseppe Paolo, Antoine Cully, Article No.: 9, pp 1–2
  • Combining Evolution and Deep Reinforcement Learning for Policy Search: A Survey
    Olivier Sigaud, Article No.: 10, pp 1–20
  • P2P Energy Trading through Prospect Theory, Differential Evolution, and Reinforcement Learning
    Ashutosh Timilsina, Simone Silvestri, Article No.: 11, pp 1–22
  • Curiosity Creates Diversity in Policy Search
    Paul-Antoine Le Tolguenec,Emmanuel Rachelson,Yann Besse,Dennis G. Wilson
    Article No.: 12, pp 1–2

Forthcoming Events

Evostar 2024 conferences are being held in Aberystwyth, Wales, United Kingdom from 3 to 5 April 2024 in hybrid mode. The venue is the Department of Computer Science on Penglais Campus at Aberystwyth University. Read more about Evostar here.

Call for Papers

GECCO 2024 @ Melbourne (hybrid)

July 14 – 18, 2024

The Genetic and Evolutionary Computation Conference (GECCO) presents the latest high-quality results in genetic and evolutionary computation since 1999. Topics include: genetic algorithms, genetic programming, swarm intelligence, complex systems, evolutionary combinatorial optimization and metaheuristics, evolutionary machine learning, learning for evolutionary computation, evolutionary multiobjective optimization, evolutionary numerical optimization, neuroevolution, real-world applications, search-based software engineering, theory, benchmarking, reproducibility, hybrids and more. Detailed Call for Paper (HTML, PDF)

Important Dates

Full papers (traditional category)

  • Abstract Deadline: January 25, 2024
  • Submission of Full Papers: February 1, 2024
  • Notification of acceptance/rejection: March 21, 2024
  • Camera-ready deadline: April 11, 2024

Poster-only papers

  • Submission of Poster-only papers: February 1, 2024
  • Notification of acceptance/rejection: March 21, 2024
  • Camera-ready deadline: April 11, 2024

GI @ ICSE 2024 Genetic Improvement Workshop

The 13th instalment of the GI workshop will take place in Lisbon, collocated with the International Conference on Software Engineering, ICSE 2024.

Authors of selected accepted papers will be invited to submit an extended version of their papers to an ASE special issue on Genetic Improvement.

Important Dates

  • Submission Deadline:  7 December 2023 (Thu)
  • Notification:  11 January 2023 (Thu)
  • Camera-ready: 25 January 2024 (Thu)
  • Workshop: 16 April 2024 (Tue)

21th Annual (2024) “Humies” Awards

For Human-Competitive Results – Produced by Genetic and Evolutionary Computation

To be held as part of the Genetic and Evolutionary Computation Conference (GECCO)
July 14-18, 2024 (Sunday – Thursday), Melbourne, Australia (Hybrid)

Detailed call for entries

Entries are hereby solicited for awards totaling $10,000 for human-competitive results that have been produced by any form of genetic and evolutionary computation (including, but not limited to genetic algorithms, genetic programming, evolution strategies, evolutionary programming, learning classifier systems, grammatical evolution, gene expression programming, differential evolution, genetic improvement, etc.) and that have been published in the open, reviewed literature between the deadline for the previous competition and the deadline for the current competition.

Important Dates

  • Friday, May 31, 2024: Deadline for entries (consisting of one TEXT file, PDF files for
    one or more papers, and possible “in press” documentation. Please
    send entries to goodman at msu dot edu
  • Friday, June 14, 2024: Finalists will be notified by e-mail
  • Friday, June 28, 2024: Finalists who will not be in Melbourne to present in person must
    submit a 10-minute video presentation to goodman at msu dot edu. Finalists who will present in person must submit a copy of their slides, for the advance use of the judges, to goodman at msu dot edu.
  • July 14-18, 2024 (Sunday – Thursday): GECCO conference
    (the schedule for the Humies session is not yet final, so please check
    the GECCO program as it is updated for the time of the Humies session).
  • Thursday, July 18, 2024: Announcement of awards at the plenary session of the GECCO conference

About this Newsletter

SIGEVOlution is the newsletter of SIGEVO, the ACM Special Interest Group on Genetic and Evolutionary Computation. To join SIGEVO, please follow this link: [WWW].
We solicit contributions in the following categories:

Art: Are you working with Evolutionary Art? We are always looking for nice evolutionary art for the cover page of the newsletter.

Short surveys and position papers. We invite short surveys and position papers in EC and EC-related areas. We are also interested in applications of EC technologies that have solved interesting and important problems.

Software. Are you a developer of a piece of EC software, and wish to tell us about it? Then send us a short summary or a short tutorial of your software.

Lost Gems. Did you read an interesting EC paper that, in your opinion, did not receive enough attention or should be rediscovered? Then send us a page about it.

Dissertations. We invite short summaries, around a page, of theses in EC-related areas that have been recently discussed and are available online.

Meetings Reports. Did you participate in an interesting EC-related event? Would you be willing to tell us about it? Then send us a summary of the event.

Forthcoming Events. If you have an EC event you wish to announce, this is the place.

News and Announcements. Is there anything you wish to announce, such as an employment vacancy? This is the place.

Letters. If you want to ask or say something to SIGEVO members, please write us a letter!

Suggestions. If you have a suggestion about how to improve the newsletter, please send us an email.

Contributions will be reviewed by members of the newsletter board. We accept contributions in plain text, MS Word, or Latex, but do not forget to send your sources and images.

Enquiries about submissions and contributions can be emailed to gabriela.ochoa@stir.ac.uk
All the issues of SIGEVOlution are also available online at: www.sigevolution.org

Notice to contributing authors to SIG newsletters
As a contributing author, you retain the copyright to your article. ACM will refer all requests for republication directly to you.

By submitting your article for distribution in any newsletter of the ACM Special Interest Groups listed below, you hereby grant to ACM the following non-exclusive, perpetual, worldwide rights:

  • to publish your work online or in print on the condition of acceptance by the editor
  • to include the article in the ACM Digital Library and in any Digital Library-related services
  • to allow users to make a personal copy of the article for noncommercial, educational, or research purposes
  • to upload your video and other supplemental material to the ACM Digital Library, the ACM YouTube channel, and the SIG newsletter site

If third-party materials were used in your published work, supplemental material, or video, make sure that you have the necessary permissions to use those third-party materials in your work

Editor: Gabriela Ochoa

Sub-editor: James McDermott

Associate Editors: Emma Hart, Bill Langdon, Una-May O’Reilly, Nadarajen Veerapen, and Darrell Whitley