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Eric 'Siggy' Scott <[log in to unmask]>
Reply To:
Evolutionary Computation Digest <[log in to unmask]>
Thu, 9 Apr 2020 08:35:36 -0400
text/plain (707 lines)
Evolutionary Computation Digest — Wednesday, 08 April 2020, Volume
34: Issue 4

SUBMISSION ADDRESS:    [log in to unmask]

(UN)SUBSCRIPTION INSTRUCTIONS:  at the bottom of this email

Today's Topics:

 - 17 April: PhD Studentship Data-Driven (Bayesian) Optimization for
Problems with Dynamic Resource Constraints
 - 22 April: PhD Position Massively Parallel Gray-box and Large Scale

 CFPs (with submission deadline)
 - 15 April (Extended): PPSN 2020 deadline postponed by 2 weeks
 - 17 April (Extended): GECCO Competition on OCP and USCP
 - 17 April (Extended): GECCO Workshops
 - 17 April (Extended): GECCO Late-Breaking Abstracts
 - 17 April (Extended): Call for Hot Off the Press - GECCO 2020
 - 30 April (Extended): Natural Computing - Special Issue on
Understanding Natural Computing Algorithm Behaviour
 - 01 May (Extended): GECCO Competition on Evolutionary Multi-Task Optimization
 - 29 May: GECCO "Humies" Awards
 - 21 June: GECCO Competition on Dynamic Stacking Optimization in
Uncertain Environments
 - 30 June: GECCO Industrial Challenge


July 8-12, 2020, ONLINE due to COVID-19 (formerly Cancún, Mexico): GECCO 2020

September 7–9, 2020, Leiden, The Netherlands: PPSN 2020

Sender: "Manuel López-Ibáñez" <[log in to unmask]>
Subject: PhD Studentship Data-Driven (Bayesian) Optimization for
Problems with Dynamic Resource Constraints

Candidates are sought for a funded PhD position in Data-Driven
(Bayesian) Optimization for Problems with Dynamic Resource Constraints
in the Decision and Cognitive Sciences Research Centre, at the
Alliance Manchester Business School, The University of Manchester. The
position is funded for a period of 4 years by EPSRC and IBM.

The PhD position is associated with an iCASE studentship with IBM and
will focus on the development of algorithms and decision support tools
for closed-loop problems with dynamic resource constraints. This
includes developing data-driven/Bayesian optimization algorithms
capable of dealing with closed-loop problems subject to constraints
that model the temporal availability of resources needed to evaluate a
candidate solution.

The PhD student will be encouraged to collaborate with peers in the
research centre and develop a wide range of skills including project
management, mentoring of interns, and presentation skills. They will
also be expected to present their work at major international
conferences and participate in events linked to the Institute for Data
Science and Artificial Intelligence at Manchester
( Both the Institute and the
Decision and Cognitive Sciences Research Centre are affiliated with
the Alan Turing Institute providing access to the resources and
network of the Institute through its fellows programme. This includes
access to data study groups and interest groups at the Turing.

The student will be supervised by Dr Richard Allmendinger
Dr Manuel Lopez-Ibanez (, Dr Jonathan Shapiro
(, and Prof Joshua Knowles
( Dr Matt Benatan
Algorithms Subgroup Lead in Machine Learning and AI at IBM Research UK
will act as external supervisor and lead the industrial input into
this research. The ICASE studentship also offers the opportunity to be
partly based at IBM Research UK.

The team at IBM Research ( is focused
on cutting-edge research in advanced Bayesian modelling, including
Bayesian Optimization, and are thus invested in how Bayesian
Optimization can be extended to challenging optimization tasks. The
project will therefore contribute directly to IBM's ongoing research
with the opportunity for influencing future IBM products.

For instructions on how to apply, please refer to:

Sender: Omar Abdelkafi <[log in to unmask]>
Subject: [2020-02428 - PhD Position F/M Massively Parallel Gray-box
and Large Scale Optimization (M/F)] Publication de l'offre d'emploi

Dear Colleagues, please consider the following PhD Offer at Inria
Lille Nord Europe, France.


Title: Massively Parallel Gray-box and Large-scale Optimization

Location: Inria Lille - Nord Europe, France

Keywords: Optimization; Evolutionary algorithms; Parallel and
distributed computing; Gray-Box Optimization; Machine learning.

Abstract: The goal of this PhD is to foster the next generation of
massively parallel large scale optimizers, by contributing to the
design of advanced and effective computational intelligence algorithms
and to set up an innovative and solid fundamental understanding of
their characteristics, while exploiting the computing power offered by
modern heterogeneous and parallel (super-computing) platforms.

The successful candidate will be part of the BONUS team at Inria
Lille, and will eventually collaborate with a number of international
and highly recognized researcher partners, in particular in the USA
(Colorado state university), and in Japan (RIKEN R-CCS).

More information about the international research environment can be
provided on-demand and in the following link :

Skills: Candidates with the following skills will be preferred:

Fluent English, excellent communication skills, keen to team working
Good background in optimization and/or evolutionary algorithms
Good background in parallel and distributed computing
Good background in machine learning
Good experience in programming

Applications: To apply, the following is mandatory: CV + application
letter + recommendation letters + references + school transcripts
Prior to application, it is recommended to contact :
[log in to unmask], [log in to unmask],
[log in to unmask]

Candidates must apply via the INRIA common platform on the link:

Sender: Carola Doerr <[log in to unmask]>
Subject: PPSN 2020 deadline postponed by 2 weeks, now April 22

PPSN 2020 update: Submission Deadlines postponed by ~2 weeks
• Abstract submission: 15 April 2020
• Paper submission: 22 April 2020

Dear Colleagues,

While several measures are now applied in most European countries to
prevent the spread of the coronavirus, several of you are facing
increasing workload, childcare, etc. We have therefore decided to
postpone the PPSN submission deadlines as indicated above. You will find
the updated Call for Papers below.

We will keep you updated on twitter (please follow @PPSNConference for
updates) and on the PPSN webpage (

Sender: Julien Lepagnot <[log in to unmask]>
Subject: GECCO 2020 Competition on OCP and USCP - Call for
participation [2nd kind reminder]


Dear colleagues,

we are pleased to invite you to take part in the 1st Competition on
the Optimal Camera Placement Problem (OCP) and the Unicost Set
Covering Problem (USCP) that will be part of the next Genetic and
Evolutionary Computation Conference (GECCO 2020, at Cancun, Mexico,
8-12 July 2020).


If an area is defined as a set of three-dimensional sample points to
be covered, and if camera configurations are sampled into so-called
candidates each with a given set of position and orientation
coordinates, then the optimal camera placement problem (OCP) comes
down to select the smallest subset of candidates which covers all the

The OCP is structurally identical to the unicost set covering problem
(USCP), which is one of Karp’s well-known NP-hard problems: given a
set of elements I (rows) to be covered, and a collection of sets J
(columns) such that the union of all sets in J is I, find the smallest
subset of J which covers I.

The main goal of this competition is to encourage innovative research
works by proposing to solve challenging OCP problem instances stated
as USCP.

The contest gathers 69 OCP problem instances: 32 of them are academic
problems (various sizes and discretizations of an empty room modeled
by a rectangular cuboid with cameras on the ceiling), and 37 of them
are real-world problems (various sizes and discretizations of urban
areas with cameras on the walls of the buildings).

Details of the competition (problem instances, data formats,
competition tracks, rules for registration, submission and
evaluation,...) are available at :


In addition to their competition entries, participants will have the
opportunity to submit a short description of their algorithm for
publication in the GECCO Companion (2-page contribution).


March 20, 2020 : Early competition registration deadline (needed for
GECCO Companion submissions)
April 17, 2020 : Submission deadline for GECCO Companion abstracts
May 1, 2020 : Notification of acceptance for GECCO Companion abstracts
May 22, 2020 : Late competition registration deadline
June 5, 2020 : End of the competition, i.e. solution submission deadline
July 8-12, 2020 : GECCO conference, and announcement of the competition results

Sender: EC-Digest Editor
Subject: GECCO Workshops

This month, the EC-Digest received CFPs for the following GECCO
Workshops.  All workshops share the EXTENDED submission deadline of 17
April, 2020.

Workshop on Evolutionary Computation for the Automated Design of Algorithms
Sender: Daniel Tauritz <[log in to unmask]>

Workshop on Green AI
Sender: Luis Martí <[log in to unmask]>

Workshop on Evolutionary Computation + Multiple Criteria Decision
Making (EC + MCDM)
Sender: Chugh, Tinkle <[log in to unmask]>

Workshop on Learning Classifier Systems
Sender: Anthony Stein <[log in to unmask]>

Good Benchmarking Practices for Evolutionary Computation
Sender: Pietro Oliveto <[log in to unmask]>

Workshop on Real-World Applications of Continuous and Mixed-integer Optimization
Sender: Pramudita <[log in to unmask]>

Workshop on Genetic Improvement
Sender: Bradley Alexander <[log in to unmask]>

Workshop on NeuroEvolution at Work
Sender: Antonio Della Cioppa <[log in to unmask]>

Parallel and Distributed Evolutionary Inspired Methods
Sender: Antonio Della Cioppa <[log in to unmask]>

Workshop on the Automated Design of Robots for the Real World
Sender: Howard, David <[log in to unmask]>

Workshop on Evolutionary Computation for Permutation Problems
Sender: Marco Baioletti <[log in to unmask]>

Workshop on Evolutionary Algorithms for Problems with Uncertainty
Sender: Alyahya, Khulood <[log in to unmask]>

Sender: Gregorio Toscano <[log in to unmask]>
Subject: Call for Late-breaking Abstracts - GECCO 2020

Late-breaking Abstracts
2020 Genetic and Evolutionary Computation Conference (GECCO 2020)
8-12 July, 2020, Cancun, Mexico.

Submission site:

Submission deadline: April 17, 2020, 23:59 (Anywhere on Earth)

Page limit: 2 pages using ACM template.

By submitting an abstract, the author(s) agree that, if their paper
is accepted, they will:

* Submit a final, revised, camera-ready version to the publisher
(May 8, 2020)
* Register at least one author to attend the conference
(May 11, 2020)
* Attend the conference (at least one author) and present the
accepted abstract at the conference.

More Information
For more information, contact the Late-Breaking Abstracts Chair, Ke Tang
<[log in to unmask]>

Sender: Gregorio Toscano <[log in to unmask]>
Subject: Call for Hot Off the Press - GECCO 2020

Hot Off the Press (HOP) track to be held as part of the
2020 Genetic and Evolutionary Computation Conference (GECCO 2020)
8-12 July, 2020, Cancun, Mexico.

Important Dates

Submission deadline:   ** April  17, 2020 **
Notification:          ** May 1, 2020 **
Camera-Ready:          ** May 8, 2020 **


The Hot Off the Press (HOP) track offers authors of recent papers the
opportunity to present their work to the GECCO community, both by giving
a talk on one of the three main days of the conference and by having a
2-page abstract appear in the Proceedings Companion, in which the
workshop papers, late-breaking abstracts, and tutorials also appear. We
invite researchers to submit summaries of their own work recently
published in top-tier conferences and journals. Contributions are
selected based on their scientific quality and their relevance to the
GECCO community. Typical contributions include (but are not limited to)
evolutionary computation papers that have appeared at venues different
from GECCO, papers comparing different heuristics and optimization
methods that have appeared at a general heuristics or optimization
venue, papers describing applications of evolutionary methods that have
appeared at venues of this application domain, or papers describing
methods with relevance to the GECCO community that have appeared at a
venue centered around this method's domain. In any case, it is the
authors' responsibility to make clear why this work is relevant for the
GECCO community, and to present the results in a language accessible to
the GECCO community.

Sender: Peter Korosec <[log in to unmask]>
Subject: CfP extension: Natural Computing - Special Issue on
Understanding Natural Computing Algorithm Behaviour

Due to COVID-19 pandemic we are extending the deadline to April 30,
2020! Please note that this is the last extension. For those who have
already submitted manuscripts, they are already in reviewing process.


Natural Computing (Impact Factor: 1.330)
Special issue on
Understanding Natural Computing Algorithm Behaviour




Understanding of optimization algorithm’s behavior is a vital part that
is needed for quality progress in the field of stochastic optimization
algorithms. Too often (new) algorithms are setup and tuned only
focusing on achieving the desired optimization goal. While this might
be effective and efficient in short term, in long term this is
insufficient due to the fact that this needs to be repeated for every
new problem that arises. Such approach provides only minor immediate
gains, instead of contributing to the progress in research on
optimization algorithms. To be able to overcome this deficiency, we
need to establish new standards for understanding optimization
algorithm behavior, which will provide understanding of the working
principles behind the stochastic optimization algorithms. This includes
theoretical and empirical research, which would lead to providing
insight into answering questions such as (1) why does an algorithm work
for some problems but does not work for others, (2) how to explore the
fitness landscape to gain better understanding of the algorithm’s
behavior, and (3) how to interconnect stochastic optimization and
machine learning to improve the algorithm’s behavior on new unseen

The focus of this special issue is to highlight theoretical and
empirical research that investigate approaches needed to analyze
stochastic optimization algorithms and performance assessment with
regard to different criteria. The main goal is to bring the problem and
importance of understanding optimization algorithms closer to
researchers and to show them how and why this is important for future
development in the optimization community. This will help
researchers/users to transfer the gained knowledge from theory into the
real world, or to find the algorithm that is best suited to the
characteristics of a given real-world problem.


This special issue seeks to provide an opportunity for researchers to
present their original contributions on understanding of optimization
algorithm behavior, and any related issues. The special issue topics
include (but are not limited to) the following:

- Data-driven approaches (machine learning/information
theory/statistics) for assessing algorithm performance
- Vector embeddings of problem search space
- Meta-learning
- New advances in analysis and comparison of algorithms
- Operators influence on algorithm behavior
- Parameters influence on algorithm behavior
- Theoretical algorithm analysis


The submitted manuscripts must not have been published or
simultaneously submitted elsewhere. For the submitted extended papers,
an extension of at least 30% beyond that in the published proceedings
is expected. Each submitted paper (extended or not) will receive
thorough reviews and evaluation. Papers will be selected based mainly
on their originality, scientific and technical quality of the
contributions, organization and presentation and relevance to the
special issue. The NACO’s submission system is already open for
submissions. When submitting your manuscript please select "Original
article" as article type. Then, later under "Additional Information”,
there is a question "Does this manuscript belong to a special issue?".
There you have to select "S.I.: Understanding Evolutionary Algorithm
Behaviour". Please submit your manuscript before April 30, 2020.

Once your manuscript is accepted, it will go into production, and will
be published in 2021.

Please ensure you read the Guide for Authors before writing your
manuscript. The Guide for Authors and link to submit your manuscript is
available on the Journal's homepage at:

Inquiries, including questions about appropriate topics, may be sent
electronically to the Guest Editors.

IMPORTANT DATES (Subject to change)

- Paper submission due: April 30, 2020
- First-round acceptance decision notification: June 30, 2020
- First revision submission due: August 31, 2020
- Second-round acceptance decision notification: November 30, 2020
- Second revision submission due: December 31, 2020
- Notification of final decision: February 28, 2021
- Target (tentative) publication date: 2021


Dr. Christian Blum (Guest Lead Editor)
Artificial Intelligence Research Institute (IIIA), Spanish National
Research Council (CSIC), Spain
Email: [log in to unmask]

Dr. Tome Eftimov
Jožef Stefan Institute, Ljubljana, Slovenia
Email: [log in to unmask]

Dr. Peter Korošec
Jožef Stefan Institute, Ljubljana, Slovenia
Email: [log in to unmask]

Sender: Eric Scott <[log in to unmask]>
Subject: GECCO Competition on Evolutionary Multi-Task Optimization


Evolutionary multitasking is an emerging concept in computational
intelligence that realises the theme of efficient multi-task
problem-solving in the domain of numerical optimization [2-5]. It is
worth noting that in the natural world, the process of evolution has,
in a single run, successfully produced diverse living organisms that
are skilled at survival in a variety of ecological niches. In other
words, the process of evolution can itself be thought of as a massive
multi-task engine where each niche forms a task in an otherwise
complex multifaceted fitness landscape, and the population of all
living organisms is simultaneously evolving to survive in one niche or
the other. Interestingly, it may happen that the genetic material
evolved for one task is effective for another as well, in which case
the scope for inter-task genetic transfers facilitates frequent leaps
in the evolutionary progression towards superior individuals. Being
nature-inspired optimisation procedures, it has recently been shown
that evolutionary algorithms (EAs) are not only equipped to mimic
Darwinian principles of “survival-of-the-fittest”, but their
reproduction operators are also capable of inducing the afore-stated
inter-task genetic transfers in multitask optimisation settings;
although, the practical implications of the latter are yet to be fully
studied and exploited in the literature. The potential efficacy of
this new perspective, as opposed to traditional approaches of solving
each optimisation problem in isolation, has nevertheless been unveiled
by so-called multi-factorial EAs (MFEAs).

For this competition, we have prepared two multi-task optimization
benchmark sets:

 —The test suite for multi-task single-objective optimization (MTSOO),
containing ten 50-task MTO benchmark problems.

  —The test suite for multi-task multi-objective optimization (MTMOO),
containing ten 50-task MTO benchmark problems.


Potential participants in this competition may target at either or
both of MTSOO and MTMOO while using all benchmark problems in the
corresponding test suites as described above for performance


May 1, 2020 (EXTENDED): submissions due

Please see the competition website for details, code, and submission

Sender: Gregorio Toscano <[log in to unmask]>
Subject: [GECCO CFP] Call For Entries 2020 Humies Awards in Cancun, Mexico

        Call For Entries
             17th Annual (2020) "Humies" Awards
                For Human-Competitive Results
        Produced by Genetic and Evolutionary Computation

                      To be Held at:
   Genetic and Evolutionary Computation Conference (GECCO)
        July 8-12, 2020 (Wednesday - Sunday) in Cancun, Mexico

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, etc.)
and that have been published in the open literature between the deadline
for the previous competition and the deadline for the current competition.

The competition will be held as part of the Genetic and Evolutionary
Computation (GECCO) conference  operated by the Association for
Computing Machinery (ACM) Special Interest Group (SIG) on Genetic and
Evolutionary Computation (SIGEVO). Entries chosen to be finalists will
be made at the conference. The winners of the awards will be announced
during the conference.

 Important Dates:

  * Friday May 29, 2020 - 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 12, 2020 - Finalists will be notified by e-mail
  * Friday June 26, 2020 - Finalists must submit their
    presentation (e.g., PowerPoint, PDF) for posting on the
    competition's web site. Send presentations to goodman at msu dot edu
  * July 8-12, 2020 (Wednesday - Sunday) - GECCO conference

Sender: Frederik Rehbach <[log in to unmask]>
Subject: Beham Andreas <[log in to unmask]>


to be held as part of the 2020 Genetic and Evolutionary Computation
Conference (GECCO 2020) organized by ACM SIGEVO

Competition webpage:

In this competition we give you a server where you can delve into
mysterious worlds of blocks and stacks. The hero in this world is a
robotic crane that is fearlessly stacking and delivering blocks. Did
you ever wish to control a WALL-E like robot solving a herculean task?
Well, now is your chance! Granted, our crane is not as cute as WALL-E,
but it wants to do a similar good job and therefore relies on YOUR
optimization skills! It waits for you to tell it which blocks to pick
up and where to drop them off so that the continuing stream of
incoming blocks is dealt with most effortlessly.

A 15 minutes getting started tutorial is available on the competition
webpage which shows how you can run a (pretty bad) sample policy. This
is implemented in three different languages: Python, C#, and Rust.
However, you can use any other programming language that you like! The
basic requirements is that you have an implementation of ZeroMQ and
Protobuf. We aimed to make the topic of optimization in dynamic or
uncertain environments as easy to approach as possible and yet create
a challenging and also rewarding experience.

We hope that you will have fun participating in this challenge! The
deadline is Sunday June, 21 2020 (anywhere in the world). For more
information please visit our webpage

Sender: Frederik Rehbach <[log in to unmask]>
Subject: GECCO Industrial Challenge 2020

GECCO Industrial Challenge 2020

--------Call for Participation-----------

The 2020 GECCO Industrial Challenge is Online and Open!

The goal of the GECCO 2020 Industrial Challenge is to develop an
optimizer for this year's industrial application in a programming
language of your choice. The optimizer has to efficiently use every
objective function evaluation as each evaluation involves a
computationally intensive fluid dynamics simulation. As usual, the
task from this industrial challenge stems directly from ongoing
cooperation with industry.
The challenge is split into two tracks. In the first track, the amount
of allowed objective function evaluations is only limited by the
computing power you are willing to invest on your own machine. In the
second track, only 100 evaluations are allowed per optimization run.
In both cases, the algorithm with the best-found objective function
value wins.

Like last year, we are able to provide the opportunity for all
participants to submit 2-page algorithm descriptions for the GECCO
Companion. Thus, it is now possible to create publications in a
similar procedure to the Late Breaking Abstracts (LBAs) directly
through competition participation!

Highlights of the GECCO 2020 Industrial Challenge include:
* Interesting Problem Domain: Reduction of emissions and environmental
pollution is more important than ever before. Efficient filters help
reduce global CO2 emissions.
* Real-world Problems: Test your algorithms and methods, directly on a
real industry problem.
* Easy Access: Easily Participate through our online platform, no
installations required.
* Fair Submission Assessment: Winners are determined automatically
through our online portal, fully objectively, only based on the final
result quality.
* Publication Possibilities: We are able to accept 2-page submissions
for the GECCO Companion, thus publications are possible directly
through competition participation.

Software and Data Availability:  Online
Challenge Submission Deadline: 2020-06-30 23:59
2-Page Algorithm Description Submission Deadline: 2020-04-03 23:59
Contact: [log in to unmask]


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 End of Evolutionary Computation Digest