newsletter of the ACM Special Interest Group on Genetic and Evolutionary Computation
Welcome to the Winter 2021 issue of the SIGEvolution newsletter! This issue is dedicated to the memory of Ingo Rechenberg, who sadly passed away on September 25th, 2021, after a short serious illness in Berlin. Recognized as the inventor of one of the branches of evolutionary computation, Evolution Strategies, Ingo Rechenberg also played a major role worldwide in establishing and promoting Bionics as a scientific discipline.
Many of us had the wonderful opportunity to attend Ingo’s inspiring keynote talk at GECCO 2019 in Prague, where he shared his passion for evolution and biologically inspired robotics and showed us the biologic and robotic versions of the Moroccan flic-flac spider, Cebrennus rechenbergi, named in his honor, as he first collected specimens in the Moroccan desert. We pay tribute to Ingo by collecting anecdotes from one of his PhD students and other members of our community touched by his work and ideas.
We continue with a lively, visual report on GECCO 2021 going virtual, and conclude the issue by announcing recent events and forthcoming calls for submissions. Please get in touch if you would like to contribute an article for a future issue or have suggestions for the newsletter.
Wishing you a Happy New year and a prosperous 2022!
Gabriela Ochoa, Editor.
Tributes to Ingo Rechenberg (1934 – 2021)
Anne Auger, Nikolaus Hansen, Manuel López-Ibáñez, Günter Rudolph
Ingo Rechenberg was one of the fathers of Evolution Strategies, my main research topic for almost two decades. I had the chance to meet him in 2005 at the GECCO conference in Washington where he presented a tutorial on “Bionik: Building on Biological Evolution”. I discovered a very passionate person who explained how biology, the flight of birds, and nature shaped and inspired his research. When discussing with him afterward, he told me that the biological motivation of the methods in our field is essential for him. He admitted that he never liked the CMA-ES algorithm (that started to be recognized at that time as a powerful method and was invented in his laboratory by Nikolaus Hansen and Andreas Ostermeier) because it is not bio-inspired enough!
A few years later, I had the chance to visit his laboratory in Berlin which looked like a scientific exhibition. The lab was full of different experiments or prototypes: windturbines, windmills, 3D models to explain Evolution Strategies… We saw the latest topic he was working on related to geckos found in the desert for which he was studying how the sand is gliding on the skin. The geckos were in a small aquarium in a large office.
I first met Ingo Rechenberg when I attended his lecture "Evolutionsstrategie I" in the late 1980s in Berlin. Rechenberg was an exciting lecturer. He was constantly marching back and forth from one end of the podium to the other, speaking with an energetic voice and visibly moved by the topic he was presenting. I learned about the 1/5th success rule and the surprisingly few assumptions we have to make such that it holds: in short, an undirected (random) change should be successful 20% of the time to be efficient. Changes with higher success rates are too conservative, changes with lower success rates are too ambitious.
In his research, Ingo Rechenberg was an engineering visionary. He envisioned a vortex wind concentrator (based on the spread wingtips of eagles) in order to generate more electricity with a smaller rotor. He envisioned producing hydrogen with bacteria on a large scale (based on bacteria he had found in the desert). He envisioned optimizing technical systems the same way biology optimizes organisms, inventing Evolution Strategies. He pursued his visions with scrutiny and passion for years or even decades.
In the mid 1990s, I joined Rechenberg's research lab and started to work on Evolution Strategies. Despite his already undoubted fame, or the Porsche in his garage, or his apparent contentment to be called "Herr Professor", he considered doctoral students as equals. We were independent, we had to be visionary, we had to invent and figure it out (as he did). He often asked for our viewpoint but he never shied away from scientifically challenging and even confronting discussions and disputes. The open and spirited atmosphere in his lab shaped and sharpened my scientific senses and resonates as of today. A remarkable insight he was able to formalize then, in our now common field of research, explained the importance of recombination in (artificial) evolution: in short, recombination allows for much larger variations without destroying as much information. Even in his latest presentation in July 2019, at the age of 84, he revealed a specific result around this idea which was new to me. Ingo Rechenberg never stopped searching and investigating and this spirit and passion shall remain with us.
Prof. Dr Ingo Rechenberg is widely regarded as one of the inventors of Evolution Strategies (ES), one of the variants of what today are more generally known as Evolutionary Algorithms (Rechenberg, 1973). As Prof. Rechenberg recounted (Rechenberg, 2000), the motivation for developing ES was to simply answer the question of: how long would it take simulated evolution to find the optimal design of an airfoil? What is notable about the early experiments carried out by Prof. Rechenberg is that they did not use a mathematical optimization model, nor a simulation implemented in a computer. These were physical experiments carried out in an actual laboratory, some of them using a wind tunnel. The evaluation of a new "solution" required manually adjusting the physical properties of the experiment and measuring new observations. Even the selection and variation steps were sometimes performed using dice rolls and a calculator (Rechenberg, 1965; Knowles, 2009). When I first learned about these experiments, I was impressed by how much of what we do today in terms of practical applications of Evolutionary Computation, Evolutionary Robotics and Bayesian Optimisation was already envisioned in these experiments by Rechenberg, decades before I was born.
GECCO 2021: Virtualization Report
Virtual venue in Gather
Group photo souvenir
Poster Room 1
Number of participants per country
Number of participants per time zone (UTC+2 is Lille’s time zone)
Usage statistics provided by Whova
Average attendance for each type of event, according to Whova
Usage statistics provided by Zoom: number of meetings/webinars, average number of participants per meetings/webinars, and total duration of meetings/webinars for each conference day
Attendance for each Gather space
Average number of views per video for each type of event
by Thomas Bäck, Pauline Bennet, Jacob de Nobel, Carola Doerr, Johann Dreo, Herilalaina Rakotoarison, Jeremy Rapin, Olivier Teytaud, Diederick Vermetten, Hao Wang, Furong Ye
Johann Dreo: Extensible Logging and Empirical Attainment Function for IOHexperimenter
In order to allow for large-scale, landscape-aware, per-instance algorithm selection (the future of solvers design) a benchmarking platform software is key. IOHexperimenter provides a large set of synthetic problems, a logging system and a fast implementation.
Pauline Bennet: Two new photonic problems for benchmarking in Nevergrad
Herilalaina Rakotoarison: From hyper-parameter tuning to the design of machine learning pipeline
Call for Submissions
Genetic and Evolutionary Computation Conference
Topics include: genetic algorithms, genetic programming, ant colony optimization and swarm intelligence, complex systems (artificial life, robotics, evolvable hardware, generative and developmental systems, artificial immune systems), digital entertainment technologies and arts, evolutionary combinatorial optimization and metaheuristics, evolutionary machine learning, evolutionary multiobjective optimization, evolutionary numerical optimization, real world applications, search-based software engineering, theory and more.
ACM SIGEVO Dissertation Award 2022
The first international conference on automated machine learning (AutoML-Conf 2022) brings together researchers and users, with the goals of developing automated methods for speeding up the development of machine learning applications, obtaining improved performance, and thereby democratizing machine learning
Parallel Problem Solving from Nature (PPSN 2022)
We invite submissions that discuss recent developments in all areas of research on, and applications of, Genetic Improvement. GI is the premier workshop in the field and provides an opportunity for researchers interested in automated program repair and software optimisation to disseminate their work, exchange ideas and discover new research directions.
Entries are hereby solicited for awards totalling $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, etc.) and that have been published in the open literature between the deadline for the previous competition and the deadline for the current competition.
Friday, May 27, 2022 — Deadline for entries (consisting of one TEXT file, PDF files for one or more papers, and possible "in press" documentation (explained below). Please send entries to goodman at msu dot edu.
Friday, June 10, 2022 — Finalists will be notified by e-mail.
Friday, June 24, 2022 — Finalists not presenting in person must submit a 10-minute video presentation (or the link and instructions for downloading the presentation, NOT a YouTube link) to goodman at msu dot edu.
July 9-13, 2022 (Saturday - Wednesday) — GECCO conference (the schedule for the Humies session is not yet final, so please check the GECCO program as it is updated).
Monday, July 11, 2022 Presentation session.
Wednesday, July 13, 2023 — Announcement of awards at the plenary session of the GECCO conference.
Judging Committee: Erik Goodman, Una-May O'Reilly, Wolfgang Banzhaf, Darrell Whitley, Lee Spector, Stephanie Forrest
Publicity Chair: William Langdon
EvoStar is comprised of four co-located conferences
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].
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Editor: Gabriela Ochoa
Sub-editor: James McDermott
Associate Editors: Emma Hart, Una-May O'Reilly, Nadarajen Veerapen, and Darrell Whitley