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EC-DIGEST-L  May 2021

EC-DIGEST-L May 2021

Subject:

EC-Digest v35n6

From:

Eric 'Siggy' Scott <[log in to unmask]>

Reply-To:

Evolutionary Computation Digest <[log in to unmask]>

Date:

Mon, 31 May 2021 09:17:36 -0400

Content-Type:

text/plain

Parts/Attachments:

Parts/Attachments

text/plain (193 lines)

Evolutionary Computation Digest — Monday, 31 May 2021, Volume
35: Issue 6

SUBMISSION ADDRESS:    [log in to unmask]
NEW LIST ARCHIVE:      http://listserv.gmu.edu/cgi-bin/wa?A0=EC-DIGEST-L

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

********************************************************************************
Today's Topics:

Announcements:
----
- New book announcement: matheuristics
- Post-doctoral position in Artificial Intelligence at University of
Deusto (Bilbao, Spain)
- 31 May: SIGEVO Summer School @ GECCO


********************************************************************************
CALENDAR OF EC-RELATED ACTIVITIES:

July 10–14, 2021, Lille, France (ONLINE-ONLY): GECCO 2021
Sep 06–08, 2021, Dornbirn, Austria (ONLINE-ONLY): FOGA 2021


********************************************************************************
Sender: Vittorio Maniezzo <[log in to unmask]>
Subject: New book announcement: matheuristics

link: https://secure-web.cisco.com/1NJUZ3tqe0uuZTUZDHV_fgpAO2tnA5-YzwELtR76PiQfHVys4B70qsTHbwpJ0tLJ9u3jwggv0w_nHH_pQRtXQ8bUkUpkmOP0o5Czk4jPnoUmH_NxrQgt7xXPN5w3xlzlRb3ar-Ph69-GHDYXdJN1CP3RRz4nCHPD3Di1iScdyxRf5xrlK0iJon_T6HKPn2C66Qat2HXt0IKMx_DYGtNUzrQ-0bWMCVxbjX-O46llLb_Hfxjcf6FOdas5b5EFo_PsRBjcknoAZE0ai6ZZyz0ycGbwb6WnFt6xWC4VBI3VF60m9_o5CYQRQuD3F-KXIyhsb_jFM8rYgtD5hKcB7A6GdstVLKNKQo4-idXhujbsfOlqNghwPGjhTABWf3avoEMGfuENQ22Djt4lKH-8OqQvteG8UR5GXmVuo-cOPjT8Qekkr6pOwyS5K211SSr8eyX4fSW9ZoHjZxq9KcxuLrYUZaqnS9nbJICAZd76TNZKKLKE/https%3A%2F%2Fwww.springer.com%2Fgp%2Fbook%2F9783030702762

This book is the first comprehensive tutorial on matheuristics.
Matheuristics are based on mathematical extensions of previously known
heuristics, mainly metaheuristics, and on original, area-specific
approaches. This tutorial provides a detailed discussion of both
contributions, presenting the pseudocodes of over 40 algorithms,
abundant literature references, and for each case a step-by-step
description of a sample run on a common Generalized Assignment Problem
example. C++ source codes of all algorithms are available in an
associated SW repository.

Table of contents (10 chapters):
The Generalized Assignment Problem      Pages 3-33
Automatic Design for Matheuristics      Pages 35-57
Single Solution Metaheuristics  Pages 61-94
Population-Based Metaheuristics Pages 95-130
Diving Heuristics       Pages 133-141
Very Large-Scale Neighborhood Search    Pages 143-158
Decomposition-Based Heuristics  Pages 159-177
Corridor Method Pages 179-187
Kernel Search   Pages 189-197
Fore-and-Back   Pages 199-211

********************************************************************************
Sender: Antonio David Masegosa Arredondo <[log in to unmask]>
Subject: Post-doctoral position in Artificial Intelligence at
University of Deusto (Bilbao, Spain)

The Deusto Institute of Technology (DeustoTech)
(https://secure-web.cisco.com/1akWfh88Pp-tQkIEa3dtiGO17HX-IMMpEmeuOYb0lQdPSOXf8_RgeSHJVW5gpOkoGZspTQy312rYzPRsFmyoJzkXwlvVCkV6YTE1cDdvSJRobRgh613uNxxC6-lXU7rgDoK_syPeomiyMrpKUBKModw5MM0noF3u8ha_UtbBjL_wFccf8U-o_KBn1KCpB2ZX5bvi7jYuPCSLtV_DPdVfuSIcTcRQfEsaDM9iiJh4DUp-eXSp-axvoADNSspCHJpuRrRV2lqkNI2QekaLVWjLrPa0_DhGsb7qqJtrEhJl2iJnZirts4OSOO3Yr8v7JFM46hYSCbVfwkPnkR5GQppIs3hEkFJAoMiuAalOx0TUpr_lYkvYTfeoxgYPW-axy5gllKfurnDI6gXidf6E7tuBfHQrzKuSqE4pa-MAvTeFs9w4qHUPpIvQmlq6C3_oP2oJRzdgsCpqp87iBBvIqcxDdeg/https%3A%2F%2Fdeustotech.deusto.es%2F) from the University of Deusto (Bilbao,
Spain) is looking for a full-time post-doctoral researcher. The position
is offered in the frame of a Horizon 2020 project. The contract will
have a duration of 3 years under a regular employment contract with full
social security coverage.

*JOB DESCRIPTION*

We are looking for a post-doctoral researcher with a solid knowledge
background in Artificial Intelligence and more specifically in the areas
of optimisation using metaheuristics and deep learning. The researcher
will perform the following functions:

- Autonomous execution of international, national and regional projects
in collaboration with the PI of the project.
- Modelling and solving optimisation problems using metaheuristic
techniques to tackle problems related to the optimisation of transport
networks.-
- Modelling and design of Deep Reinforcement Learning techniques for the
optimisation of transport networks.
- Design and development of software modules for deploying optimisation
models based on metaheuristics or Deep Reinforcement Learning models in
production.
- Drafting of reports and writing of scientific papers.
- Support in interaction and communication with the rest of the project
partners.
- Technical and scientific development of research projects and
subsequent transfer in your area.
- Support in the materialisation of project proposals and offers from a
scientific-technical point of view.
- Support in the administrative management of the projects in which
he/she is involved.

*REQUIREMENTS*

*General requirements*

- University degree in computer engineering, telecommunications
engineering, computer science, applied mathematics or similar.
- At least two years of experience as a PhD in areas related to
optimisation using metaheuristics and/or application of deep learning
techniques.
- Solid technical and research background, proven through publications
in journals indexed in the Journal Citations Report.
- Solid programming experience in Java and/or Python.
- Experience in deep learning frameworks such as Tensorflow, Keras,
Pytorch, etc.
- Experience in executing research projects related to the application
of Artificial Intelligence techniques.
- Fluent in English (written and spoken).

It will be also considered as a plus:

- Experience in the application of Artificial Intelligence techniques in
the area of transport and, preferably, in the optimisation of transport
networks.
- Previous experience in the use and application of optimisation
techniques based on surrogate models.
- Previous experience in the use and application of Deep Reinforcement
Learning techniques.
- Previous participation in H2020 and/or FP7 projects.

*APPLICATION PROCESS*

Please, use this link to apply for the position:

https://secure-web.cisco.com/1oXH9XxNNjVvXAEYV5I15vb99rXgjbwWCqMxgxTFgYyXGmo8JsoU95jQPazoyJkq6yV-BcpiULz1BVRynX3W7JyoZyxkVQOjsouSiHcIVZ-GTUdrevtd49e9vDO6m6zvcaDu9y-Rc15rU0ykV2OGPquKge9ur_XOaUDnIBnct7WkgaT2L02TiDcQK6c0jX5p8163S8H-p6TqlwFm9BRyi6a8-06S9QPgPbsnWRwGgekXYJ2sQBH6pzuzMwhw6M8xNUMk851wlnuS-6gUUhWr6HykVyE4dSdrLwGtwqye3mMToCN1WJ2LKi-3vll_K5TwNxxcpPFPbkcsCOkRfF0hXIzQGJh1Q9RCoDSloYJj4ipXg2PS3p_xNKVSUVZzyY8bg8NfxZMIE9ekmaecgLRahrckTbDgrA3scqzr_T2vyPl35rddE4zfnriN3ATQ_nbFKYuqkNw73_DR2vW56sZP-RQ/https%3A%2F%2Fdeusto.talentclue.com%2Fen%2Fnode%2Fadd%2Fcv%2Fjob%2F70296276%2Fcompany%2F28742110%2F4590%3Fclicked_button%3Dapply_manually

********************************************************************************
Sender: "Christine Zarges [chz8] (Staff)" <[log in to unmask]>
Subject: Call for Applications: SIGEVO Summer School (5 - 9 July 2021)

SIGEVO Summer School
Held in conjunction with ACM GECCO 2021 Conference
5 - 9 July 2021 (online on Zoom and Gather)

Website: https://secure-web.cisco.com/18vYUIbC2OCkw8jvPZkVn_HGziJlcAQEJp0VogZhF3Hdw0adcWMhd755yaNGQVGJVUGDsjdZ4nG9w2CD9eqXykjFzQ_BhkMwlUQIQnNUA2W0A91_-px2aKQutMrrfyFqtOnhj2RpKYGkh_XpfjZWavG2Tqpy-IWb_3O5IHQ4GvRs3eApvMNap4UGloBq209M_k13fZszsqI0Iz3gz5SZ9XEtSyNlqYki8ljlBQajZlaMkOFbQkGc6O6N92g7ULSZnywSjQeGSvcFVmRO667w2dgyioxAROow9Km5AwR9vrMxXayqO1J-Idbuy_fEWMVmKsyYwU_WHaKTsU-JYAjQbZf6dthFO54HhK_1ZB2CBIYX24SyaO5Rl8DlI3twcLU2wCKsnsZeHrfWfA91uu47RNJiwYRuYezFMH0gmg74_cuSvguWI_9Rt9_JWsu_whRMilU1Qe9mJhK5X_joUvubsKw/https%3A%2F%2Fgecco-2021.sigevo.org%2FSummer-School
Email: [log in to unmask]

Application deadline: 31 May 2021

The SIGEVO Summer School (S3, https://secure-web.cisco.com/18vYUIbC2OCkw8jvPZkVn_HGziJlcAQEJp0VogZhF3Hdw0adcWMhd755yaNGQVGJVUGDsjdZ4nG9w2CD9eqXykjFzQ_BhkMwlUQIQnNUA2W0A91_-px2aKQutMrrfyFqtOnhj2RpKYGkh_XpfjZWavG2Tqpy-IWb_3O5IHQ4GvRs3eApvMNap4UGloBq209M_k13fZszsqI0Iz3gz5SZ9XEtSyNlqYki8ljlBQajZlaMkOFbQkGc6O6N92g7ULSZnywSjQeGSvcFVmRO667w2dgyioxAROow9Km5AwR9vrMxXayqO1J-Idbuy_fEWMVmKsyYwU_WHaKTsU-JYAjQbZf6dthFO54HhK_1ZB2CBIYX24SyaO5Rl8DlI3twcLU2wCKsnsZeHrfWfA91uu47RNJiwYRuYezFMH0gmg74_cuSvguWI_9Rt9_JWsu_whRMilU1Qe9mJhK5X_joUvubsKw/https%3A%2F%2Fgecco-2021.sigevo.org%2FSummer-School)
goes into round 4 with a renewed format covering both, general skills needed
by GECCO researchers and in-depth knowledge about specific GECCO topics.

Networking with fellow students and senior researchers (mentors) will give
participants a unique opportunity to connect with the community.

Interested? Places are limited. Submit a short motivational statement
by 31 May 2021.
More information: https://secure-web.cisco.com/1TlwfPry34B0etAynRr8DnWqhg2ODo7dMTaK5xpwb6w7DLYbmFbJsU7e2mtBE7LrBGfnijLMOgDRIvtVbo8SjDq9SI-ezWMfvK9v3GQskSjpEpshnWEbvLlWaD4crl42IFwmKSmLSsIeJZJqlft0EpbybPXpJEWFH9j653Kt84oBxyaZn5ExaD4oUXEQKy9vD9DGMiCBqW3fG5-rlg3zx7yFMdn1OxNLOgAFulO9Kjhnh08TrnkB30S2cckR9JHilSo_qmPj_nPT-QNaHY18vHdW1w5cKBVZjZfe6ETKk-F4zd2fnYGkCTbn7P-uhcd84U_c98a4l-WGkP0kVDbNT96NT5ONOiLxh9VW1xwLXzSbNq4hpnASuUDASQrdNUmEZGdzp_Xc4WQr2kOVmsGFcUmjHFkSpRbVHVV3biw3fdvdDEEDySy7620kvc7vRZk9ZAPh89Mo9KBESUTuCs03HtQ/https%3A%2F%2Fgecco-2021.sigevo.org%2FCall-for-Summer-School

Organisers:
- Miguel Nicolau, University College Dublin, Republic of Ireland
- Christine Zarges, Aberystwyth University, Wales, United Kingdom

Questions? Send us an email: [log in to unmask]

Mentors:
- Juergen Branke, University of Warwick, England, United Kingdom
- Carola Doerr, Sorbonne University, France
- Carlos Fonseca, University of Coimbra, Portugal
- Erik Hemberg, MIT Computer Science and Artificial Intelligence Lab,
United States
- Manuel López-Ibáñez, University of Málaga, Spain
(more to be confirmed)


********************************************************************************
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********************************************************************************
 End of Evolutionary Computation Digest
********************************************************************************

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