Important Dates

26/10/2018 Calls
21/1/2019 Paper Submission
7/3/2019 Decision Notification
8/4/2019 Early Bird Paper Registration
10/6/2019 CEC 2019
  • Special session proposals ( extended ):  26 October, 2018    9 November, 2018
  • Competition proposals ( extended ):  26 November, 2018    10 December, 2018
  • Workshop proposals( extended ):  7 January, 2019    21 January, 2019 (Further extended)
  • Tutorial proposals: 7 January, 2019
  • Paper submission: 21 January, 2019 (NOTE: Due to the large number of requests, IEEE CIS has just approved to keep the CEC 2019 paper submission system open until 31 January 2019. You are welcomed to update or submit new papers by this date. )
  • Decision notification: 7 March, 2019
  • Camera ready paper due: 8 April, 2019
  • Notification of Presentation Format (oral or poster): 15 April, 2019
  • Early bird Registration Deadline: 8 April, 2019
  • Conference: 10 June, 2019
  • Note: all deadlines are 11:59pm Anywhere on earth.



Public Lecture




Xin Yao

Chair Professor

Department of Computer Science and Engineering
Southern University of Science and Technology
and
CERCIA, School of Computer Science
University of Birmingham

Title: What Can Evolutionary Computation Do For You?

Time: 1 hour

Evolutionary computation refers to the study of computational systems that use ideas and get inspirations from natural systems, especially biological systems. Its primary goal is to develop more robust, reliable and self-adaptive computational systems that help to tackle complex optimisation and learning problems in the real world, from routing a fleet of trucks in a dynamically changing road networks to scheduling a large team of software engineers to develop a complex software system, from calibrating engines of cars to design artificial neural networks for pattern recognition and prediction. This talk tries to explain what evolutionary computation is, how it works and why it is interesting from both scientific research's point of view and practical application's point of view. Examples will be given throughout the talk to illustrate the potential benefits (and weakness) of evolutionary computation.

Prof. Xin Yao (M’91-SM’96-F’03) is a Chair Professor of Computer Science at the Southern University of Science and Technology, Shenzhen, China, and a Professor of Computer Science at the University of Birmingham, UK. He is an IEEE Fellow, and a Distinguished Lecturer of IEEE Computational Intelligence Society (CIS). His major research interests include evolutionary computation, ensemble learning, and their applications in software engineering. He has been working on multi-objective optimisation since 2003, when he published a well-cited EMO’03 paper on many objective optimisation. His research won the 2001 IEEE Donald G. Fink Prize Paper Award, 2010, 2016, and 2017 IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION Outstanding Paper Awards, 2010 BT Gordon Radley Award for Best Author of Innovation (Finalist), 2011 IEEE TRANSACTIONS ON NEURAL NETWORKS Outstanding Paper Award, and many other best paper awards. He received the prestigious Royal Society Wolfson Research Merit Award in 2012 and the IEEE CIS Evolutionary Computation Pioneer Award in 2013. He was the the President (2014-15) of IEEE CIS, and the Editor-in-Chief (2003-08) of IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION.



Plenary Talks




Hisao Ishibuchi

Chair Professor

Department of Computer Science and Engineering
Southern University of Science and Technology

Title: Research Trends in Evolutionary Multi-Objective Optimization: Past, Present and Future

Time: 1 hour

Evolutionary multi-objective optimization (EMO) has been a hot research area since the 1990s. Optimization problems usually have multiple objectives even if they are traditionally handled by single-objective optimization. The goal of this talk is to clearly explain that EMO is still a young research area with a number of interesting and promising research topics. First, past research trends are explained from a viewpoint of the relation between test problems and EMO algorithms. Easy test problems with respect to the convergence were mainly used in the 1990s. As a result, non-elitist EMO algorithms were proposed together with sophisticated diversification mechanisms since those test problems were difficult with respect to the diversification. In the 2000s, elitist EMO algorithms based on the Pareto dominance relation were proposed to handle difficult test problems with respect to the convergence. In the 2010s, a number of many-objective algorithms were proposed using the framework of decomposition-based EMO algorithms since many-objective test problems have regular Pareto fronts. Next, current research trends in the design of EMO algorithms are explained, which are motivated by test problems with irregular Pareto fronts, large-scale test problems, and expensive test problems. Then, difficulties of fair performance comparison of EMO algorithms are explained: Performance comparison results depend on various factors such as the population size, available computation time, and reference point specifications in the hypervolume and IGD indicators as well as the choice of test problems. Finally, some promising future research directions are suggested, which include the implementation of fair performance comparison, the design of any-time EMO algorithms with an unbounded archive population, and the selection of a small number of candidate solutions as well as applications to machine learning and artificial intelligence.

Prof. Hisao Ishibuchi received the BS and MS degrees from Kyoto University in 1985 and 1987, respectively. In 1992, he received the Ph. D. degree from Osaka Prefecture University where he was a professor since 1999. From April 2017, he is with Department of Computer Science and Engineering, Southern University of Science and Technology (SUSTech), Shenzhen, China as a Chair Professor. He received Best Paper Awards from GECCO 2004, HIS-NCEI 2006, FUZZ-IEEE 2009, WAC 2010, SCIS & ISIS 2010, FUZZ-IEEE 2011, ACIIDS 2015, GECCO 2017, 2018, and EMO 2019. He also received a 2007 JSPS (Japan Society for the Promotion of Science) Prize and a 2019 IEEE CIS Fuzzy Systems Pioneer Award. He was the IEEE CIS Vice-President for Technical Activities (2010-2013), an IEEE CIS Distinguished Lecturer (2015-2017), and the President of the Japan Evolutionary Computation Society (2016-2018). Currently, he is the Editor-in-Chief of IEEE Computational Intelligence Magazine (2014-2019) and an IEEE CIS AdCom member (2014-2019). He is also an Associate Editor of IEEE TEVC, IEEE Access, and IEEE T-Cyb. He is an IEEE Fellow. In 2018, he was selected in the “Recruitment Program of Global Experts for Foreign Experts” known as the “Thousand Talents Program” in China.


Emma Hart

Professor

School of Computing
Edinburgh Napier University

Title: Towards the Autonomous Evolution of Robotic Ecosystems

Time: 1 hour

From its very beginnings, Evolutionary Computation has been used as a tool to evolve artefacts, starting with the very first optimisation of a joint plate at the Technical University of Berlin in 1965, quickly followed by evolution of the often-cited two-phase nozzle in1968. Since then, advances in computing (CPU power, simulation engines), materials science, and engineering (3D printing, automated assembly) have considerably enhanced our ability to evolve artefacts: these range from design of functional objects such as satellite antennae, through creative design of chairs and tables, to design of active artefacts such as robots that dynamically sense and interact with their surroundings.
In this talk, I will give a brief history of the evolution of artefacts, leading to evolutionary robotics, and finally to a recent collaborative project that aims to deliver a disruptive robotic technology in which robots are created, reproduce and evolve in real-time and real space. The long-term vision is of a technology that enables the evolution of entire autonomous robotic ecosystems that live and work for long periods in challenging and dynamic environments without the need for direct human oversight, e.g. in outer-space, or decommissioning a nuclear reactor. Rather than being designed and manufactured, the morphologies and control processes of robots will emerge as a result of evolutionary processes, continuously changing both their form and behaviour over-time.

Prof. Emma 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 in Natural Computation. She is active world-wide in the field of Evolutionary Computation, for example as General Chair of PPSN 2016, and as a Track Chair at GECCO for several years. She has given keynotes at EURO 2016 and UKCI 2015, as well as invited talks and tutorials at many Universities and international conferences. She is Editor-in-Chief of Evolutionary Computation (MIT Press) from January 2016 and an elected member of the ACM SIGEVO Executive Board. She is also a member of the UK Operations Research Society Research Panel.


Risto Miikkulainen

Professor

The University of Texas at Austin
and
Cognizant Technology Solutions

Title: Creative AI through Evolutionary Computation

Time: 1 hour

Last decade has seen tremendous progress in Artificial Intelligence (AI). AI is now in the real world, powering applications that have a large practical impact. Most of it is based on modeling what is already known, e.g. predicting what the right classification of an image or a language sequence might be. The next step for AI is machine creativity, e.g. designing engineering solutions are more complex, perform better, or at a lower cost than existing solutions. Evolutionary computation is likely to play a central role in future such AI. I will review several recent techniques and applications where evolutionary creativity improves upon best human solutions, including designing neural network architectures, web interfaces, and growth recipes for agriculture.

Prof. Risto Miikkulainen is a Professor of Computer Science at the University of Texas at Austin and Associate VP of Evolutionary AI at Cognizant. He received an M.S. in Engineering from Helsinki University of Technology (now Aalto University) in 1986, and a Ph.D. in Computer Science from UCLA in 1990. His current research focuses on methods and applications of neuroevolution, as well as neural network models of natural language processing and vision; he is an author of over 400 articles in these research areas. Risto is an IEEE Fellow, and his work on neuroevolution has recently been recognized with the Gabor Award of the International Neural Network Society and Outstanding Paper of the Decade Award of the International Society for Artificial Life.



Plenary Industry Talks




Wei Cui

Co-founder/Chief Scientist

Squirrel AI Learning
by
Yixue Group

Title: How AI Makes Personalized Education Affordable to Every Family in China

Time: 1 hour

Yixue Squirrel AI is an AI driven adaptive education platform that is revolutionising the education industry and expanding fast at multiple fronts. In this talk I will introduce the AI techniques used inside of Squirrel AI and the product evaluation process. Key components of the system will be explained, including a student model, a pedagogy model, a domain model, and a prediction model. I will also cover the techniques used in Squirrel AI such as Genetic Algorithms, Machine Learning, Information Theory, Bayesian methods, Neural Networks, Graph theory and Probabilistic Graphical Model.

Dr. Wei Cui is a co-founder and Chief Scientist of Squirrel AI Learning by Yixue Group, the leading AI + adaptive education innovator at the forefront of AI revolution. Squirrel AI Learning has established more than 1,800 learning centres in China within 4 years, and is included in the TOP 20 Chinese AI Unicorn Companies in 2018.
Dr. Cui led the development of Squirrel AI intelligent adaptive learning system, the pioneering AI-powered adaptive learning system for K-12 students in China, which has been proved to achieve a better effect at teaching than expert human-teachers in a series of certified human-vs-AI competitions.
Dr. Cui holds a PhD and was a postdoctoral fellow in artificial intelligence and algorithmic trading. He has published over 20 peer-reviewed academic papers and articles in areas of AI, agent-based modelling, complex adaptive system, quantitative finance and AI education. Dr. Cui was awarded MIT Technology Review "35 Innovators Under 35 China” in 2018.

Advisory Board

Organising Committee

General Co-Chair

Mengjie Zhang

General Co-Chair

Kay Chen Tan

Technical Co-Chair

Jürgen Branke

Technical Co-Chair

Oscar Cordón

Technical Co-Chair

Hisao Ishibuchi

Technical Co-Chair

Jing Liu

Technical Co-Chair

Gabriela Ochoa

Technical Co-Chair

Dipti Srinivasan

Plenary Talk Chair

Yaochu Jin

Special Session Chair

Chuan-Kang Ting

Tutorial Chair

Xiaodong Li

Competition Chair

Jialin Liu

Workshop Chair

Handing Wang

Submission Chair

Huanhuan Chen

Sponsor Chair

Andy Song

Poster Chair

Kai Qin

Industry Co-chair

Wei Cui

Industry Co-chair

Xiaoying Gao

Publicity Co-Chair

Stefano Cagnoni

Publicity Co-Chair

Anna I Esparcia-Alcázar

Publicity Co-Chair

Emma Hart

Publicity Co-Chair

Bin Hu

Publicity Co-Chair

Sanaz Mostaghim

Publicity Co-Chair

Yew-Soon Ong

Publicity Co-Chair

Jun Zhang

Publicity Co-Chair

Tomohiro Yoshikawa

Finance Chair

Bing Xue

Local Organising Chair

Will Browne

Local Organising Chair

Hui Ma

Registration Chair

Aaron Chen

Proceedings Chair

Yi Mei

Web Master

Yiming Peng

Local Organizing Member

Harith Al-Sahaf

Local Organizing Member

Qi Chen

Local Organizing Member

Yanan Sun

Local Organizing Member

Ying Bi

Sponsors

Travel & Transportation


Conference Venue

Museum of New Zealand Te Papa Tongarewa

55 Cable Street, PO Box 467
Wellington, 6011
New Zealand




Transportation (Directions)

On foot

In central Wellington you’re rarely more than 20 minutes’ walk from Te Papa. You’ll find us on the waterfront, right in the heart of Wellington.

Enjoy a five-minute stroll from the Wellington i-SITE Visitor Information Centre, or a 20-minute walk from the railway station. Many bus stops in the city are just a few minutes’ walk away too.

By bus

Most Wellington buses (including those from the airport and railway station) stop along Courtenay Place and Willis Street. From these stops, it’s just a few minutes’ walk to Te Papa.

Private tour and ‘hop on, hop off’ buses stop outside the museum.

Metlink bus routes and timetables

Parking at Te Papa for tour coaches and buses

By bike or kick scooter

The waterfront around Te Papa is bicycle and kick scooter friendly. Please don't bring them inside the museum but park your bike or kick scooter at our racks – found behind Quake Braker, near our main entrance. We offer a lock for your scooter for purchase at a cost of $7.

Parking at Te Papa

By train

From Wellington Railway Station it’s a 20-minute walk to Te Papa. Alternatively, you can catch a bus or taxi nearby.

Metlink train routes and timetables

By car

Take the Aotea Quay exit when driving south into central Wellington along the SH1 motorway. Continue along Waterloo, Customhouse, and Jervois Quays, which lead directly into Cable Street and Te Papa’s convenient car park.

Parking at Te Papa

Taxis

Wellington Combined Taxis have a stand outside Te Papa.

From the cruise ship terminal

Wellington downtown map

Shuttle bus from the cruise ship terminal

Your cruise ship may run a shuttle bus to the Wellington i-SITE Visitor Information Centre. From there it’s just five minutes’ walk to Te Papa via the waterfront.

Walk from the terminal

On a fine day, enjoy a 30- to 45-minute stroll along the beautiful Wellington waterfront, straight from the cruise ship terminal. Simply follow the harbour’s edge and you can’t miss us.

From the airport

You can catch a taxi or Airport Flyer bus from Wellington Airport to Te Papa, or follow the directions below to drive here.

Driving to Te Papa from Wellington Airport

Driving time: About 15 minutes

  1. Follow SH1, turning left along Cobham Drive.
  2. At the Evans Bay Parade lights, turn right. Continue around the harbour and along Oriental Parade.
  3. Turn right into Wakefield Street.
  4. Turn right into Taranaki Street, then right again into Cable Street. Te Papa’s car park is on your left.