BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:Australia/ACT BEGIN:DAYLIGHT DTSTART:20211003T030000 TZOFFSETFROM:+1000 TZOFFSETTO:+1100 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=10 TZNAME:AEDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20210404T020000 TZOFFSETFROM:+1100 TZOFFSETTO:+1000 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=4 TZNAME:AEST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20210928T081818Z UID:F513C5EF-6782-4F75-A5A4-54D3DB3EAF47 DTSTART;TZID=Australia/ACT:20210928T170000 DTEND;TZID=Australia/ACT:20210928T180000 DESCRIPTION:IEEE Computational Intelligence Society\, Australian Capital Te rritory (ACT) chapter invites you to:\n\nVirtual Seminar - 28 September 20 21\, 5-6pm AEST\n\nSpeaker: Professor Jonathan Fieldsend\, Department of C omputer Science\, University of Exeter\, UK\n\nTitle: Network visualisatio ns of multi-objective optimisation landscapes\n\n===\n\nAbstract: Local op tima networks (LONs) have been developed over the last two decades to repr esent the landscape of (single objective) optimisation problems. In a LON\ , graph vertices represent local optima in the search domain\, their radii the basin sizes\, and directed edges between vertices the ability to tran sit from one basin to another (with the edge width denoting how easy this is). Recently\, analogues of these network visualisations have been develo ped to visualise multi-objective landscapes. In this talk we discuss three different approaches for representing multi-objective landscapes with net works that have recently been developed: (i) the PLOS-net (Pareto Optimal Solutions Network)\, which uses an undirected graph\, representing mutuall y non-dominating solutions and neighbouring links\, but not basin sizes\; (ii) the DNON (Dominance Neutral Optima Network) which utilises point-base d Pareto hill-climbing to determine dominance neutral optima to construct a directed graph\; and (iii) the PLON (Pareto Local Optima Network)\, whic h utilises set-based (Pareto local optima) representation to define mode c onstruction. These alternative formulations will be compared on some illus trative problems\, and some of the underlying computational issues in cons tructing LONs in a multi-objective as opposed to uni-objective problem dom ain will be discussed. The inherent issue of local dominance neutrality is also seen to be particularly important in such visualisations\, as each a vertex in DNON and PLON constructs is typically a set. We also illustrate how using combinations of the alternative network visualisations can ofte n grant additional insight to a problem.\n\n===\n\nSpeaker biography: Jona than Fieldsend is Professor of Computational Intelligence\, Director of Re search\, and Academic Lead of the Optimisation Group in the Department of Computer Science at the University of Exeter\, UK. He graduated with a BA in Economics from the Durham University in 1998\, an MSc in Computational Intelligence from the University of Plymouth in 1999 and a PhD in Computer Science from the University of Exeter in 2003. He has published widely in the technical literature\, mainly on multi-objective optimisation\, and i ts interface with machine learning. He has an h-index of 24 and i100-index of 10. His work has received £8M in grant funding spanning UKRI\, indust ry and charities\, including over £1M as Principal Investigator. He is an Associate Editor/Editorial Board Member of IEEE Transactions on Evolution ary Computation\, ACM Transactions on Evolutionary Learning and Optimizati on\, and Complex and Intelligent Systems. He is vice-Chair of the IEEE Com putational Intelligence Society Task Force on Data-Driven Evolutionary Opt imization of Expensive Problems and also vice-Chair of the IEEE Computatio nal Intelligence Society Task Force on Multi-Modal Optimization. He was co -Chair of the Evolutionary Multi-criterion Optimization (EMO) Track at GEC CO 2019 and GECCO 2020\, and is Editor-in-Chief of GECCO 2022.\n\n======== ====\n\nSpeaker(s): Prof. Jonathan Fieldsend\, \n\nVirtual: https://events .vtools.ieee.org/m/281430 LOCATION:Virtual: https://events.vtools.ieee.org/m/281430 ORGANIZER:h.singh@adfa.edu.au SEQUENCE:5 SUMMARY:IEEE CIS ACT Chapter Seminar: Network visualisations of multi-objec tive optimisation landscapes URL;VALUE=URI:https://events.vtools.ieee.org/m/281430 X-ALT-DESC:Description: <br /><p>IEEE Computational Intelligence Society\, Australian Capital Territory (ACT) chapter invites you to:&nbsp\;</p>\n<p> Virtual Seminar - 28 September 2021\, 5-6pm AEST</p>\n<p>Speaker: Professo r Jonathan Fieldsend\, Department of Computer Science\, University of Exet er\, UK</p>\n<p>Title: Network visualisations of multi-objective optimisat ion landscapes</p>\n<p>===</p>\n<p>Abstract:&nbsp\; Local optima networks (LONs) have been developed over the last two decades to represent the land scape of (single objective) optimisation problems. In a LON\, graph vertic es represent local optima in the search domain\, their radii the basin siz es\, and directed edges between vertices the ability to transit from one b asin to another (with the edge width denoting how easy this is). Recently\ , analogues of these network visualisations have been developed to visuali se multi-objective landscapes. In this talk we discuss three different app roaches for representing multi-objective landscapes with networks that hav e recently been developed: (i) the PLOS-net (Pareto Optimal Solutions Netw ork)\, which uses an undirected graph\, representing mutually non-dominati ng solutions and neighbouring links\, but not basin sizes\; (ii) the DNON (Dominance Neutral Optima Network) which utilises point-based Pareto hill- climbing to determine dominance neutral optima to construct a directed gra ph\; and (iii) the PLON (Pareto Local Optima Network)\, which utilises set -based (Pareto local optima) representation to define mode construction. T hese alternative formulations will be compared on some illustrative proble ms\, and some of the underlying computational issues in constructing LONs in a multi-objective as opposed to&nbsp\;uni-objective problem domain will be discussed. The inherent issue of local dominance neutrality is also se en to be particularly important in such visualisations\, as each a vertex in DNON and PLON constructs is typically a set. We also illustrate how usi ng combinations of the alternative network visualisations can often grant additional insight to a problem.&nbsp\;</p>\n<p>===</p>\n<p>Speaker biogra phy: <span lang="EN-AU" xml:lang="EN-AU" data-contrast="auto">Jonathan Fie ldsend is Professor of Computational Intelligence\, Director of Research\, and Academic Lead of the Optimisation Group in the Department of Computer Science at the University of Exeter\, UK. He graduated with a BA in Econo mics from the Durham University in 1998\, an MSc in Computational Intellig ence from the University of Plymouth in 1999 and a PhD in Computer Science from the University of Exeter in 2003. He has published widely in the tec hnical literature\, mainly on multi-objective optimisation\, and its inter face with machine learning. He has an h-index of 24 and i100-index of 10. His work has received &pound\;8M in grant funding spanning UKRI\, industry and charities\, including over &pound\;1M as Principal Investigator. He i s an Associate Editor/Editorial Board Member of IEEE Transactions on Evolu tionary Computation\, ACM Transactions on Evolutionary Learning and Optimi zation\, and Complex and Intelligent Systems. He is vice-Chair of the IEEE Computational Intelligence Society Task Force on Data-Driven Evolutionary Optimization of Expensive Problems&nbsp\;and also&nbsp\;vice-Chair of the IEEE Computational Intelligence Society Task Force on Multi-Modal Optimiz ation. He was co-Chair of the Evolutionary Multi-criterion Optimization (E MO) Track at GECCO 2019 and GECCO&nbsp\;2020\, and&nbsp\;is Editor-in-Chie f of GECCO 2022.</span>&nbsp\;</p>\n<p>============</p> END:VEVENT END:VCALENDAR