Xiaodong Li, General Chair
RMIT University, Australia | webpage
Xiaodong Li received his B.Sc. degree from Xidian University, Xi'an, China, and Ph.D. degree in Artificial Intelligence from University of Otago, Dunedin, New Zealand, respectively. He is a Professor with the School of Computing Technologies, RMIT University, Melbourne, Australia. His research interests include machine learning, evolutionary computation, neural networks, deep learning, data analytics, multiobjective optimization, operational research, and swarm intelligence. He served as an Associate Editor of the IEEE Transactions on Evolutionary Computation, Swarm Intelligence (Springer), and International Journal of Swarm Intelligence Research. He is a founding member of IEEE CIS Task Force on Swarm Intelligence, a former vice-chair of IEEE Task Force on Multi-modal Optimization, and a former chair of IEEE CIS Task Force on Large Scale Global Optimization. He is the recipient of 2013 ACM SIGEVO Impact Award and 2017 IEEE CIS IEEE Transactions on Evolutionary Computation Outstanding Paper Award. He is an IEEE Fellow.
Julia Handl, Editor-in-Chief
Alliance Manchester Business School, University of Manchester, UK | webpage
Julia Handl obtained a Bsc (Hons) in Computer Science from Monash University in 2001, an MSc degree in Computer Science from the University of Erlangen-Nuremberg in 2003, and a PhD in Bioinformatics from the University of Manchester in 2006. From 2007 to 2011, she held an MRC Special Training Fellowship at the University of Manchester, and she is now a Professor in Decision Sciences at Alliance Manchester Business School. A core strand of her work explores the use of multiobjective optimization in unsupervised and semi-supervised classification. She has developed multiobjective algorithms for clustering and feature selection tasks in these settings, and her work has highlighted some of the theoretical and empirical advantages of this approach.
Markus Wagner, Local Chair
University of Adelaide, Australia | webpage
Markus Wagner is an Associate Professor at the School of Computer Science, University of Adelaide, Australia. He has done his PhD studies at the Max Planck Institute for Informatics in Saarbruecken, Germany and at the University of Adelaide, Australia. For the outcomes of his studies, he has received the university's Doctoral Research Medal - the first for his school - and three best paper awards. His research topics range from mathematical runtime analysis of heuristic optimisation algorithms and theory-guided algorithm design to applications of heuristic methods to renewable energy production, professional team cycling and software engineering. So far, he has been a program committee member 80+ times, and he has written 150+ articles with 200+ different co-authors. He is an ACM Lifetime Member, is on SIGEVO's Executive Board and serves as the first ever Sustainability Officer. He has contributed to GECCOs as Workshop Chair and Competition Chair, and he has chaired several education-related committees within the IEEE CIS, where he also served as founding chair of two task forces.
Mario Garza-Fabre, Proceedings
Cinvestav, Mexico | webpage
Mario Garza-Fabre received the M.Sc. and Ph.D. degrees in Computer Science from the Center for Research and Advanced Studies (Cinvestav), Campus Tamaulipas, Mexico, in 2009 and 2014, respectively. From 2015 to 2018, he was a Research Associate at the University of Manchester and Liverpool John Moores University, UK. He joined Cinvestav, Campus Tamaulipas in 2018, where currently holds the position of Associate Professor in Optimization and Computational Intelligence. His main research interests involve the analysis and design of (meta-)heuristic optimization techniques, as well as their application to challenging problems from diverse areas such as bioinformatics, data mining/machine learning, transportation, and communications.
Kate Smith-Miles, Publicity
The University of Melbourne | webpage
Kate Smith-Miles is a Melbourne Laureate Professor of Applied Mathematics in the School of Mathematics and Statistics at The University of Melbourne, and Director of the ARC Industrial Transformation Training Centre for Optimisation Technologies, Integrated Methodologies and Applications (OPTIMA). She is also Associate Dean (Enterprise and Innovation) for the Faculty of Science at The University of Melbourne. Prior to joining The University of Melbourne in September 2017, she was Professor of Applied Mathematics at Monash University, Head of the School of Mathematical Sciences (2009-2014), and inaugural Director of the Monash Academy for Cross & Interdisciplinary Mathematical Applications (MAXIMA) from 2013-2017. Previous roles include President of the Australian Mathematical Society (2016-2018), and membership of the Australian Research Council College of Experts (2017-2019). Kate obtained a B.Sc(Hons) in Mathematics and a Ph.D. in Electrical Engineering, both from The University of Melbourne. Commencing her academic career in 1996, she has published 2 books on neural networks and data mining, and over 280 refereed journal and international conference papers in the areas of neural networks, optimisation, data mining, and various applied mathematics topics. She has supervised 30 PhD students to completion, and has been awarded over AUD$20 million in competitive grants, including 13 Australian Research Council grants and industry awards. She was awarded a Georgina Sweet Australian Laureate Fellowship from the Australian Research Council (2014-2020), enabling her Instance Space Analysis methodology to be expanded into an online tool (MATILDA, Melbourne Algorithm Test Instance Library with Data Analytics). Kate was elected a Fellow of the Australian Academy of Science in 2022, a Fellow of the Institute of Engineers Australia (FIEAust) in 2006, and a Fellow of the Australian Mathematical Society (FAustMS) in 2008. Awards include: the Australian Mathematical Society Medal in 2010 for distinguished research; the EO Tuck Medal from ANZIAM in 2017 for outstanding research and distinguished service; the Ren Potts Medal for outstanding research in the theory and practice of operations research from the Australian Society for Operations Research (ASOR) in 2019; and the Monash University Vice-Chancellor’s Award for Excellence in Postgraduate Supervision in 2012. In addition to her academic activities, she also regularly act as a consultant to industry in the areas of optimisation, data mining, and intelligent systems. She is also actively involved in mentoring, particularly with the aim of encouraging greater female participation in mathematics, and she chairs the Advisory Board for the AMSI Choose Maths program.
Richard Allmendinger, Publicity
The University of Manchester, UK | webpage
Richard Allmendinger is Professor of Applied AI at the Alliance Manchester Business School (AMBS) and Associate Dean for Business Engagement of the Faculty of Humanities, The University of Manchester (UoM), and Turing Fellow at the Alan Turing Institute. Richard is also an Advisor for River Capital Ltd, and Senior Scientist at Eharo Ltd. His expertise is in the development and application of sequential decision-making methods to problems with multiple objectives, uncertainties and resourcing issues arising in areas such as healthcare, manufacturing, engineering, music, sports, and finance. Richard has attracted £39M+ in grant funding as PI/co- I from UKRI, industry, and other sources, is an Editor for several AI journals, and has served in numerous chair roles for different AI conferences. He is an External Examiner at Warwick Business School and a former UoM Director of the ESRC- funded CDT in Data Analytics & Society.
Ying Bi, Student Affairs
Zhengzhou University, China | webpage
Ying Bi is currently a professor at Zhengzhou University, China. She received her PhD degree in 2020 from the Victoria University of Wellington (VUW), New Zealand. Her research focuses mainly on evolutionary computer vision and machine learning. She has published an authored book on genetic programming for image classification and over 50 papers in fully refereed journals and conferences. Dr Bi is currently the Vice-Chair of the IEEE CIS Task Force on Evolutionary Computer Vision and Image Processing, and a member of the IEEE CIS Task Force on Evolutionary Computation for Feature Selection and Construction. She is serving as the workshop chair of IEEE CEC 2024, organizer of the EDMML workshop in IEEE ICDM 2023, 2022, and 2021, and co-chair of the special session on ECVIP at IEEE CEC 2023, 2022 and IEEE CIMSIVP at IEEE SSCI 2023, 2022. She is serving as guest editor for two international journals. She has been serving as an organizing committee member of IEEE CEC 2019 and Australasian AI 2018, PC member/reviewer of over ten conferences and a reviewer of over twenty international journals.
Grant Dick, Student Affairs
University of Otago
Amir H Gandomi, Student Workshop Chair
University of Technology Sydney | webpage
Amir H. Gandomi is a Professor of Data Science and an ARC DECRA Fellow at the Faculty of Engineering & Information Technology, University of Technology Sydney. He is also affiliated with Obuda University, Budapest, as a Distinguished Professor. Prior to joining UTS, Prof. Gandomi was an Assistant Professor at Stevens Institute of Technology, and a distinguished research fellow at BEACON center, Michigan State University. Prof. Gandomi has published over three hundred journal papers and 12 books which collectively have been cited 44,000+ times (H-index = 94). He has been named as one of the most influential scientific minds and received the Highly Cited Researcher award (top 1% publications and 0.1% researchers) from Web of Science for six consecutive years, from 2017 to 2022. In the recent most impactful researcher list, done by Stanford University and released by Elsevier, Prof Amir H Gandomi is ranked among the top 1,000 researchers (top 0.01%) and top 50 researchers in AI and Image Processing subfield in 2021! He also ranked 17th in GP bibliography among more than 15,000 researchers. He has received multiple prestigious awards for his research excellence and impact, such as the 2023 Achenbach Medal and the 2022 Walter L. Huber Prize, the highest-level mid-career research award in all areas of civil engineering. He has served as associate editor, editor, and guest editor in several prestigious journals, such as AE of IEEE Networks and IEEE IoTJ. Prof Gandomi is active in delivering keynotes and invited talks. His research interests are global optimisation and (big) data analytics using machine learning and evolutionary computations in particular.
Marcella Scoczynski Ribeiro Martins, Electronic Media
Federal University of Technology - Paraná, Brazil | webpage
Marcella Scoczynski is an Assistant Professor at Federal University of Technology - Parana UTFPR, Brazil. She has done her PhD on Computer Engineering at Federal University of Technology - Parana UTFPR, Brazil. Her thesis has awarded at the Theses Competition during Brazilian Conference on Intelligent Systems (BRACIS 2018) and at the Theses Contest during 5th IEEE Latin American Conference on Computational Intelligence (LA-CCI 2018). Her main research interests are numerical and combinatorial optimization, evolutionary computation and metaheuristics (with a particular interest in estimation of distribution algorithms), and landscape analysis. She co-authored scientific papers in international journals and conferences.
Hirad Assimi, Electronic Media
University of Adelaide, Australia | webpage
Hirad completed his B.Sc and M.Sc degrees in Mechanical Engineering from the University of Guilan and pursued his PhD in Computer Science at the University of Adelaide. He has a keen interest in applying Evolutionary Computation to real-world complex optimisation problems, particularly within the mining and energy sectors. Currently, Hirad is a postdoctoral researcher working on the Mine Operational Vehicle Electrification project.
Nadarajen Veerapen, SIGEVO Eletronic Media Affairs
Université de Lille, France
Nadarajen Veerapen is an Associate Professor (maître de conférences) at the University of Lille, France. Previously he was a research fellow at the University of Stirling in Scotland. He holds a PhD in Computing Science from the University of Angers, France, where he worked on adaptive operator selection. His research interests include local search, hybrid methods, search-based software engineering and visualisation. He is the Electronic Media Chair for GECCO 2021 and has served as Electronic Media Chair for GECCO 2020, Publicity Chair for GECCO 2019 and as Student Affairs Chair for GECCO 2017 and 2018. He has previously co-organised the workshop on Landscape-Aware Heuristic Search at PPSN 2016, GECCO 2017-2019.
Yuan Sun, Hybridization (Visualization)
La Trobe Business School, La Trobe University | webpage
Yuan Sun is a Lecturer in Business Analytics and Artificial Intelligence at La Trobe University, Australia. He received his BSc in Applied Mathematics from Peking University, China, and his PhD in Computer Science from The University of Melbourne, Australia. His research interests include artificial intelligence, machine learning, operations research, and evolutionary computation. His research has contributed significantly to the emerging area of leveraging machine learning for combinatorial optimisation. He is the vice-chair of the IEEE task force on large-scale global optimisation and has organised special sessions and workshops, and delivered tutorials at the GECCO, PPSN, and CEC conferences.
Mario Andrés Muñoz, Hybridization (Visualization)
School of Computer and Information Systems, The University of Melbourne, Australia. | webpage
Mario Andrés Muñoz is a Research Fellow at the School of Computer and Information Systems, The University of Melbourne; and the ARC Training Centre in Optimisation Technologies, Integrated Methodologies and Applications (OPTIMA). He received the B.Eng. and M.Eng. degrees in Electronics Engineering from Universidad del Valle, Colombia, in 2005 and 2008 respectively, and the Ph.D. degree in Engineering from The University of Melbourne, Australia, in 2014. His research interests focus on the application of optimisation, computational intelligence, signal processing, data analysis, and machine learning methods to ill-defined science, engineering and medicine problems.
Nguyen Su, Hybrid Scheduling
RMIT University, Australia | webpage
Su Nguyen is a Senior Lecturer in AI and Analytics at RMIT University, Australia. He received his Ph.D. degree in Artificial Intelligence and Operations Research from Victoria University of Wellington (VUW), Wellington, New Zealand, in 2013. His expertise includes simulation-optimization, evolutionary computation, automated algorithm design, interfaces of artificial intelligence and operations research, and their applications in logistics, energy, and transportation. Nguyen has a strong track record in developing simulation models, simulation-based decision support tools, and simulation-optimisation algorithms for industry applications. He has 70+ publications in top peer-reviewed journals and conferences in computational intelligence and operations research. His current research focuses on hybrid intelligence systems that combine the power of modern artificial intelligence technologies and operations research methodologies. He was the chair (2014-2018) of IEEE task force on Evolutionary Scheduling and Combinatorial Optimisation and is a member of IEEE CIS Data Mining and Big Data technical committee. He delivered tutorials about evolutionary simulation-optimisation and AI-based visualisation at Parallel Problem Solving from Nature Conference (2018), IEEE World Congress on Computational Intelligence (2020), and Genetic and Evolutionary Computation Conference (2022).
Dhananjay Thiruvady, Hybrid Scheduling
Deakin University, Australia | webpage
Dhananjay Thiruvady is a Senior Lecturer in Industry Projects at Deakin University, Australia. Since completing his Ph.D. in Optimisation (Monash University, 2012), he has worked at the interface of academia and industry. He develops techniques in artificial intelligence and operations research and applies these in the application areas of mining, logistics, health, biosecurity and land-use. He has published over 55 papers in high-ranked conferences and journals in evolutionary computation and operations research. He has previously been on the program committee of the Australasian Data Mining Conference (AusDM), the International Conference on Swarm Intelligence (ANTS), the Genetic and Evolutionary Computation Conference (GECCO), and Hybrid Metaheuristics. His teaching spans industry-oriented units to algorithms and computer programming, for which he has achieved multiple awards.
Andy Song, Sponsorships
School of Computing Technologies, RMIT University
Frank Neumann, Sponsorship
University of Adelaide, Australia | webpage
Frank Neumann is a professor and the leader of the Optimisation and Logistics group at the University of Adelaide and an Honorary Professorial Fellow at the University of Melbourne. His current position is funded by the Australian Research Council through a Future Fellowship and focuses on AI-based optimisation methods for problems with stochastic constraints. Frank has been the general chair of the ACM GECCO 2016 and co-organised ACM FOGA 2013 in Adelaide. He is an Associate Editor of the journals """"Evolutionary Computation"""" (MIT Press) and ACM Transactions on Evolutionary Learning and Optimization. In his work, he considers algorithmic approaches in particular for combinatorial and multi-objective optimization problems and focuses on theoretical aspects of evolutionary computation as well as high impact applications in the areas of cybersecurity, renewable energy, logistics, and mining.
Mingyu Guo, Poster
The University of Adelaide | webpage
Mingyu Guo is a Lecturer in the Optimisation and Logistics group at the University of Adelaide. He received his Ph.D. degree in Computer Science from Duke University, USA. Prior to joining the University of Adelaide, he was a Lecturer in the Economics and Computation group at University of Liverpool, UK. His main research focus is algorithmic game theory and its application to cyber security, as well as combinatorial optimisation via neural networks and evolutionary computation.
Bach Long Nguyen, Poster
Bach Long Nguyen is a Research Fellow at the University of Adelaide, Australia, and he has been a visiting scholar at Monash University, Australia. His research interests include optimisation and online learning for maritime transport, and vehicular edge computing in intelligent transport systems.
Nguyen Dang, Workshops
University of St Andrews, UK
Nguyen Dang is a Senior Research Fellow at the University of St Andrews (UK). Her research interests include automated algorithm configuration, algorithm selection, dynamic algorithm configuration (DAC), and the applications of such methods to the domain of constraint programming.
Mohammad Nabi Omidvar, Workshops
University of Leeds, United Kingdom
Nabi Omidvar is a University Academic Fellow (Assistant Professor) with the School of Computing, University of Leeds, and Leeds University Business School, UK. He is an expert in large-scale global optimization and is currently a senior member of the IEEE and the chair of IEEE Computational Intelligence Society's Taskforce on Large-Scale Global Optimization. He has made several award winning contributions to the field including the state-of-the-art variable interaction analysis algorithm which won the IEEE Computational Intelligence Society's best paper award in 2017. He also coauthored a paper which won the large-scale global optimization competition in the IEEE Congress on Evolutionary Computation in 2019. Dr. Omidvar's current research interests are high-dimensional (deep) learning and the applications of artificial intelligence in financial services.
Mengjie Zhang, Tutorials
Victoria University of Wellington, New Zealand | webpage
Mengjie Zhang is a Fellow of Royal Society of New Zealand, a Fellow of IEEE, and currently Professor of Computer Science at Victoria University of Wellington, where he heads the interdisciplinary Evolutionary Computation Research Group. He is a member of the University Academic Board, a member of the University Postgraduate Scholarships Committee, Associate Dean (Research and Innovation) in the Faculty of Engineering, and Chair of the Research Committee of the Faculty of Engineering and School of Engineering and Computer Science. His research is mainly focused on evolutionary computation, particularly genetic programming, particle swarm optimisation and learning classifier systems with application areas of feature selection/construction and dimensionality reduction, computer vision and image processing, evolutionary deep learning and transfer learning, job shop scheduling, multi-objective optimisation, and clustering and classification with unbalanced and missing data. He is also interested in data mining, machine learning, and web information extraction. Prof Zhang has published over 700 research papers in refereed international journals and conferences in these areas. He has been serving as an associated editor or editorial board member for over 10 international journals including IEEE Transactions on Evolutionary Computation, IEEE Transactions on Cybernetics, the Evolutionary Computation Journal (MIT Press), ACM Transactions on Evolutionary Learning and Optimisation, Genetic Programming and Evolvable Machines (Springer), IEEE Transactions on Emergent Topics in Computational Intelligence, Applied Soft Computing, and Engineering Applications of Artificial Intelligence, and as a reviewer of over 30 international journals. He has been a major chair for eight international conferences. He has also been serving as a steering committee member and a program committee member for over 80 international conferences including all major conferences in evolutionary computation. Since 2007, he has been listed as one of the top ten world genetic programming researchers by the GP bibliography (http://www.cs.bham.ac.uk/~wbl/biblio/gp-html/index.html). He is the Tutorial Chair for GECCO 2014, an AIS-BIO Track Chair for GECCO 2016, an EML Track Chair for GECCO 2017, and a GP Track Chair for GECCO 2020 and 2021. Since 2012, he has been co-chairing several parts of IEEE CEC, SSCI, and EvoIASP/EvoApplications conference (he has been involving major EC conferences such as GECCO, CEC, EvoStar, SEAL). Since 2014, he has been co-organising and co-chairing the special session on evolutionary feature selection and construction at IEEE CEC and SEAL, and also delivered a keynote/plenary talk for IEEE CEC 2018,IEEE ICAVSS 2018, DOCSA 2019, IES 2017 and Chinese National Conference on AI in Law 2017. Prof Zhang was the Chair of the IEEE CIS Intelligent Systems Applications, the IEEE CIS Emergent Technologies Technical Committee, and the IEEE CIS Evolutionary Computation Technical Committee; a Vice-Chair of the IEEE CIS Task Force on Evolutionary Computer Vision and Image Processing, and the IEEE CIS Task Force on Evolutionary Deep Learning and Applications; and also the founding chair of the IEEE Computational Intelligence Chapter in New Zealand.
Emma Hart, Tutorials
Edinburgh Napier University
Prof. Hart gained a 1st Class Honours Degree in Chemistry from the University of Oxford, followed by an MSc in Artificial Intelligence from the University of Edinburgh. Her PhD, also from the University of Edinburgh, explored the use of immunology as an inspiration for computing, examining a range of techniques applied to optimisation and data classification problems. She moved to Edinburgh Napier University in 2000 as a lecturer, and was promoted to a Chair in 2008 where she leads a group in Nature-Inspired Intelligent Systems, specialising in optimisation and learning algorithms applied in domains that range from combinatorial optimisation to robotics. Her work mainly involves development of algorithms inspired by biological evolution to discover novel solutions to challenging problems. She was appointed as Editor-in-Chief of Evolutionary Computation (MIT Press) in 2017. She has been invited to give keynotes at major international conferences including CLAIO 2020, IEEE CEC 2019, EURO 2016 and UKCI 2015 and was General Chair of PPSN 2016, and as a Track Chair at GECCO for several years. She is an elected member of the Executive Board of the ACM SIG on Evolutionary Computation. More broadly, she invited member of the UK Operations Research Society Research Panel, and in Scotland, co-leads the Artificial Intelligence theme within SICSA. She was appointed as a panel member for REF2021 (UoA11 Computer Science). In 2020 she was appointed to the Steering Committee that developed Scotland's AI Strategy published in 2021 . She has a sustained track record of obtaining funding from the EU, EPSRC and of engaging with industry via KTP projects and consultancy, and participates enthusiastically in public-engagement activity, e.g Pint of Science. Her work in evolutionary robotics has attracted significant media attention, e.g. in New Scientist, the Guardian, Telegraph and the Conversation. In 2021, she gave a TED Talk on Evolutionary Robotics, available online
Hemant Kumar Singh, Competitions
University of New South Wales | webpage
Hemant Kumar Singh is an Associate Professor at the School of Engineering and Technology at the University of New South Wales (UNSW), Australia. He completed his PhD from UNSW in 2011 and B.Tech in Mechanical Engineering from Indian Institute of Technology (IIT) Kanpur in 2007. He worked with General Electric Aviation at John F. Welch Technology Centre as a Lead Engineer during 2011-13. His research interests include development of evolutionary computation methods to deal with various challenges such as multiple objectives, constraints, uncertainties, hierarchical (bi-level) objectives, and decision-making. He has co-authored over 125 refereed publications on these topics collectively. He is an Associate Editor for IEEE Transactions on Evolutionary Computation and has been in the organizing team of several conferences, e.g., IEEE CEC (Program co-chair 2021), SSCI (MCDM co-chair 2020-23), ACM GECCO (RWACMO workshop co-chair 2018-21). More details of his research and professional activities can be found at his website.
Justyna Petke, Late Breaking Abstracts
University College London | webpage
Justyna Petke is a Principal Research Fellow and Proleptic Associate Professor, conducting research in genetic improvement. She has a doctorate in Computer Science from University of Oxford and is now at the Centre for Research on Evolution, Search and Testing (CREST) at University College London. Her work on genetic improvement was awarded a Silver and a Gold 'Humie' at GECCO 2014 and GECCO 2016. She also organised several Genetic Improvement Workshops. She currently serves on the editorial board of the Genetic Programming and Evolvable Machines (GPEM), Empirical Software Engineering (EMSE), and Automated Software Engineering (ASE) journals.
Will N. Browne, Late Breaking Abstract
Queensland University of Technology, Australia
Prof. Will Browne's research focuses on applied cognitive systems. Specifically, how to use inspiration from natural intelligence to enable computers/machines/robots to behave usefully. This includes cognitive robotics, learning classifier systems, and modern heuristics for industrial application. Prof. Browne has been co-track chair for the Genetics-Based Machine Learning (GBML) track and the co-chair for the Evolutionary Machine Learning track at the Genetic and Evolutionary Computation Conference. He has also provided tutorials on Rule-Based Machine Learning and Advanced Learning Classifier Systems at GECCO, chaired the International Workshop on Learning Classifier Systems (LCSs), and lectured graduate courses on LCSs. He has co-authored the first textbook on LCSs Introduction to Learning Classifier Systems, Springer 2017. Currently, he is Professor and Chair in Manufacturing Robotics at Queensland University of Technology, Brisbane, Queensland, Australia.
Marcus Gallagher, Hot-off-the-Press
University of Queensland | webpage
Marcus Gallagher is an Associate Professor in the Artificial Intelligence Group in the School of Information Technology and Electrical Engineering. His research interests are in artificial intelligence, including optimisation and machine learning. He is particularly interested in understanding the relationship between algorithm performance and problem structure via benchmarking. My work includes cross-disciplinary collaborations and real-world applications of AI techniques. Dr Gallagher received his BCompSc and GradDipSc from the University of New England, Australia in 1994 and 1995 respectively, and his PhD in 2000 from the University of Queensland, Australia. He also completed a GradCert (Higher Education) in 2010.
Erik Goodman, Humies (Chair)
Michigan State University and BEACON Center for the Study of Evolution in Action, USA | webpage
Erik D. Goodman is PI and Executive Director of the BEACON Center for the Study of Evolution in Action, an NSF Science and Technology Center headquartered at Michigan State University, funded by NSF for 2010-20, and now continuing with funding from MSU. BEACON has a dynamic research program and extensive education and outreach programs, and includes evolutionary biologists as well as computer scientists/engineers studying evolutionary computation (for search and optimization) and evolution of digital organisms. Goodman is a professor in Electrical and Computer Engineering, Mechanical Engineering, and Computer Science and Engineering. He was co-founder and VP Technology, Red Cedar Technology, Inc., (now a division of Siemens), which developed design optimization software that has become a best-selling system in industry. He was named Michigan Distinguished Professor of the Year, 2009, and received the MSU Distinguished Faculty Award in 2011. He was elected Chair of the Executive Board (2003-2005) and Senior Fellow, International Society for Genetic and Evolutionary Computation; then was Founding Chair of the ACM SIG on Genetic and Evolutionary Computation (SIGEVO), 2005. His current personal research is on evolutionary algorithms for optimization of heterogeneous propellant grains for solid-fuel rockets and on evolutionary approaches to neural architecture search.
William B. Langdon, Humies (Publicity Chair)
University College London, UK | webpage
William B. Langdon has been working on GP since 1993. His PhD was the first book to be published in John Koza and Dave Goldberg's book series. He has previously run the GP track for GECCO 2001 and was programme chair for GECCO 2002 having previously chaired EuroGP for 3 years. More recently he has edited SIGEVO's FOGA and run the computational intelligence on GPUs (CIGPU) and EvoPAR workshops. His books include A Field Guide to Genetic Programming, Foundations of Genetic Programming and Advances in Genetic Programming 3. He also maintains the genetic programming bibliography. His current research uses GP to genetically improve existing software, CUDA, search based software engineering and Bioinformatics.
Miguel Nicolau, Summer School
University College Dublin, Ireland | webpage
Miguel is a Lecturer in Business Analytics, in the School of Business of University College Dublin, Ireland. His research interests revolve around Artificial Intelligence, Machine Learning, Evolutionary Computation, Business Analytics, Genetic Programming, and Real-World Applications. He is a senior member of the UCD's NCRA (Natural Computing Research & Applications) group.
Anna V Kononova, Summer School
LIACS, Leiden University, The Netherlands
Anna V. Kononovais an Assistant Professor at the Leiden Institute of Advanced ComputerScience. She received her MSc degree in Applied Mathematics from Yaroslavl State University (Russia) in 2004 and PhD degree in Computer Science from University of Leeds (UK) in 2010. After a total of 5 years of postdoctoral experiences at Technical University Eindhoven (The Netherlands) and Heriot-Watt University (Edinburgh, UK), Anna has spent a number of years working as a mathematician in industry. Her current research interests include analysis of optimisation algorithms and machine learning.
Michael Kirley, Summer School
University of Melbourne
Bing Xue, Women@GECCO
Victoria University of Wellington, New Zealand | webpage
Bing Xue is currently Professor of Artificial Intelligence, and Deputy Head of School in the School of Engineering and Computer Science at Victoria University of Wellington. Her research focuses mainly on evolutionary computation, machine learning, big data, feature selection/learning, evolving neural networks, explainable AI and their real-world applications. Bing has over 300 papers published in fully refereed international journals and conferences including many highly cited papers and top most popular papers. Bing is currently the Editor of IEEE CIS Newsletter, Chair of the Evolutionary Computation Technical Committee, member of ACM SIGEVO Executive Committee and Chair of IEEE CIS Task Force on Evolutionary Deep Learning and Applications. She also chaired the IEEE CIS Data Mining and Big Data Technical Committee, Students Activities committee, and a member of many other committees. She founded and chaired IEEE CIS Task Force on Evolutionary Feature Selection and Construction, and co-founded and chaired IEEE CIS Task Force on Evolutionary Transfer Learning and Transfer Optimisation. She also won a number of awards including Best Paper Awards from international conferences, and Early Career Award, Research Excellence Award and Supervisor Award from her University, IEEE CIS Outstanding Early Career Award, IEEE TEVC Outstanding Associate Editor and others. Bing has also been served as an Associate/Guest Editor or Editorial Board Member for > 10 international journals, including IEEE TEVC, ACM TELO, IEEE TETCI, IEEE TAI, and IEEE CIM. She is a key organiser for many international conferences, e.g. Conference Chair of IEEE CEC 2024, Co-ambassador for Women in Data Science NZ 2023, Tutorial Chair for IEEE WCCI 2022, Publication Chair of EuroGP 2022, Track Chair for ACM GECCO 2019-2022, Workshop Chair for IEEE ICDM 2021, General Co-Chair of IVCNZ 2020, Program Co-Chair for KETO 2020, Senior PC of IJCAI 2019-2021, Finance Chair of IEEE CEC 2019, Program Chair of AJCAI 2018, IEEE CIS FASLIP Symposium founder and Chair since 2016, and others in international conferences. More can be seen from her website.
Aldeida Aleti, Women@GECCO
Monash University | webpage
Aldeida's research interests are in the area of Automated Software Engineering, which aims at creating machines that write software, from requirements elicitation, to design, code generation, testing, and finally code repair. This involves the application and advancement of novel Artificial Intelligence and optimisation techniques.
Boris Naujoks, Job Market
Cologne University of Applied Sciences, Germany | webpage
Boris Naujoks is a professor for Applied Mathematics at TH Köln - Cologne University of Applied Sciences (CUAS). He joint CUAs directly after he received his PhD from Dortmund Technical University in 2011. During his time in Dortmund, Boris worked as a research assistant in different projects and gained industrial experience working for different SMEs. Meanwhile, he enjoys the combination of teaching mathematics as well as computer science and exploring EC and CI techniques at the Campus Gummersbach of CUAS. He focuses on multiobjective (evolutionary) optimization, in particular hypervolume based algorithms, and the (industrial) applicability of the explored methods.
Tea Tušar, Job Market
Jožef Stefan Institute, Slovenia | webpage
Tea Tušar is a research fellow at the Department of Intelligent Systems of the Jozef Stefan Institute in Ljubljana, Slovenia. She was awarded the PhD degree in Information and Communication Technologies by the Jozef Stefan International Postgraduate School for her work on visualizing solution sets in multiobjective optimization. She has completed a one-year postdoctoral fellowship at Inria Lille in France where she worked on benchmarking multiobjective optimizers. Her research interests include evolutionary algorithms for singleobjective and multiobjective optimization with emphasis on visualizing and benchmarking their results and applying them to real-world problems.
Hongjie Xu, Team Member
RMIT School of Computing and Technologies
Hongjie Xu is a first year PhD student in Computer Science. His research interests include Combinatorial optimization and Machine learning.
Andrew Ciezak, Team Member
University of Newcastle
Andrew Ciezak has a background in computer science and hardware engineering, and he is now working towards a PhD at the University of Newcastle in symbolic regression and genetic approaches. He has a decade of experience in industry and balances his academic pursuits with a practical spirit so that he is always entertaining interesting problems.
Ruimin Chu, Team Member
RMIT School of Computing and Technologies
Ruimin Chu is a PhD candidate in the School of Computing Technologies at RMIT University. Her research interests include time series analysis, machine learning, explainable AI as well as their application to address real-world problems.
Yiliao Song, Team Member
RMIT University, Australia | webpage
With a PhD in computer science, Dr Yiliao Song is currently working as a Research Fellow at the RMIT University. Her research focuses on streaming data mining, trustworthy AI. %%%%%%
Yunzhuang Shen, Team Member
RMIT School of Computing and Technologies
Yunzhuang Shen recently received his Ph.D. in computer science from RMIT University. His research explores the potential of machine learning in enhancing traditional optimization methods, such as integer programming and metaheuristics.
Yameng Peng, Team Member
RMIT School of Computing and Technologies
Yameng Peng received his Master of Data Science in 2019, and is currently pursuing his PhD in Computer Science at RMIT University, Australia. His research interests are neuroevolution (evolutionary neural architecture search), deep learning, computer vision etc.
Kendall Taylor, Team Member
RMIT University, Australia
Currently developing automated agents for online auctions at RMIT University, Dr. Kendall Taylor's research interests include evolutionary algorithms, reinforcement learning and behavioural economics. Kendall received his PhD in Computer Science from RMIT University in 2022 on the subject of multi-objective optimisation and preference learning.
Vic Ciesielski, Team Member
RMIT University, Australia | webpage
Vic has a PhD from Rutgers University and is an associate professor at RMIT University. His research interests are in genetic programming, machine learning and evolutionary art.
Anne Auger, Business Committee
Inria, France | webpage
Anne Auger is a research director at the French National Institute for Research in Computer Science and Control (Inria) heading the RandOpt team. She received her diploma (2001) and PhD (2004) in mathematics from the Paris VI University. Before to join INRIA, she worked for two years (2004-2006) at ETH in Zurich. Her main research interest is stochastic continuous optimization including theoretical aspects, algorithm designs and benchmarking. She is a member of ACM-SIGECO executive committee and of the editorial board of Evolutionary Computation. She has been General chair of GECCO in 2019. She has been organizing the biannual Dagstuhl seminar """"Theory of Evolutionary Algorithms"""" in 2008 and 2010 and all seven previous BBOB workshops at GECCO since 2009. She is co-organzing the forthcoming Dagstuhl seminar on benchmarking.
Manuel López-Ibáñez, Business Committee
University of Manchester, UK | webpage
Dr. López-Ibáñez is a senior lecturer in the Decision and Cognitive Sciences Research Centre at the Alliance Manchester Business School, University of Manchester, UK. Between 2020 and 2022, he was also a """"Beatriz Galindo"""" Senior Distinguished Researcher at the University of Málaga, Spain. He received the M.S. degree in computer science from the University of Granada, Granada, Spain, in 2004, and the Ph.D. degree from Edinburgh Napier University, U.K., in 2009. He has published 32 journal papers, 9 book chapters and 54 papers in peer-reviewed proceedings of international conferences on diverse areas such as evolutionary algorithms, multi-objective optimization, and various combinatorial optimization problems. His current research interests are experimental analysis and the automatic configuration and design of stochastic optimization algorithms, for single and multi-objective problems. He is the lead developer and current maintainer of the irace software package for automatic algorithm configuration (http://iridia.ulb.ac.be/irace) and the EAF package for the analysis of multi-objective optimizers (https://mlopez-ibanez.github.io/eaf/).