BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:Asia/Shanghai BEGIN:STANDARD DTSTART:19910915T010000 TZOFFSETFROM:+0900 TZOFFSETTO:+0800 TZNAME:CST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20251110T040625Z UID:DEFBE429-67EB-4D86-8A89-1B816D08E55C DTSTART;TZID=Asia/Shanghai:20251108T170000 DTEND;TZID=Asia/Shanghai:20251108T173000 DESCRIPTION:Black-box optimization problems are common in real-world applic ations\, yet their unknown mathematical forms make it difficult to analyze problem features\, understand algorithm behavior\, and design efficient a lgorithms. If these problems could be made more transparent through visual ization\, algorithm design and selection would become more intuitive. This report presents the Nearest-Better Network (NBN)\, a novel and powerful f ramework for fitness landscape analysis (FLA) applicable to both continuou s and combinatorial optimization problems. Unlike existing FLA methods tha t often rely on algorithm-dependent or operator-biased sampling\, NBN can be constructed using uniform sampling. In this directed graph\, each solut ion points to its nearest fitter neighbor\, thereby preserving essential s tructural characteristics of the landscape—such as modality\, ruggedness \, neutrality\, and basin-of-attraction organization. We establish a theor etical foundation showing that NBN effectively approximates the maximum tr ansition probability network under simple evolutionary dynamics. Moreover\ , to overcome the high computational cost of earlier implementations\, we propose efficient algorithms with time complexities of O(NlogN) for both c ontinuous and sequence-based problems\, enabling large-scale landscape ana lyses. Comprehensive case studies on benchmark problems—including the On eMax function and the Traveling Salesman Problem (TSP)—demonstrate that NBN can reveal previously hidden landscape properties and diagnose the wea knesses of state-of-the-art algorithms such as EAX\, LKH\, and NLKH.\n\nSp eaker(s): Yiya Diao\, \n\nFunction Room 2\, 6th Floor\, Shenzhen Nanshan G enpla Hotel\, No. 3333 Liuxian Avenue\, Nanshan District\, Shenzhen\, Guan gdong\, China LOCATION:Function Room 2\, 6th Floor\, Shenzhen Nanshan Genpla Hotel\, No. 3333 Liuxian Avenue\, Nanshan District\, Shenzhen\, Guangdong\, China ORGANIZER:ranchengcn@gmail.com SEQUENCE:6 SUMMARY:Nearest-Better Network: A Unified Tool for Visualizing the Fitness Landscapes of Continuous and Combinatorial Problems URL;VALUE=URI:https://events.vtools.ieee.org/m/510369 X-ALT-DESC:Description: <br /><p class="MsoNormal" style="line-height: norm al\;"><span lang="EN-US" style="font-family: 'Times New Roman'\,serif\;">B lack-box optimization problems are common in real-world applications\, yet their unknown mathematical forms make it difficult to analyze problem fea tures\, understand algorithm behavior\, and design efficient algorithms. I f these problems could be made more transparent through visualization\, al gorithm design and selection would become more intuitive. This report pres ents the Nearest-Better Network (NBN)\, a novel and powerful framework for fitness landscape analysis (FLA) applicable to both continuous and combin atorial optimization problems. Unlike existing FLA methods that often rely on algorithm-dependent or operator-biased sampling\, NBN can be construct ed using uniform sampling. In this directed graph\, each solution points t o its nearest fitter neighbor\, thereby preserving essential structural ch aracteristics of the landscape&mdash\;such as modality\, ruggedness\, neut rality\, and basin-of-attraction organization. We establish a theoretical foundation showing that NBN effectively approximates the maximum transitio n probability network under simple evolutionary dynamics. Moreover\, to ov ercome the high computational cost of earlier implementations\, we propose efficient algorithms with time complexities of O(NlogN) for both continuo us and sequence-based problems\, enabling large-scale landscape analyses. Comprehensive case studies on benchmark problems&mdash\;including the OneM ax function and the Traveling Salesman Problem (TSP)&mdash\;demonstrate th at NBN can reveal previously hidden landscape properties and diagnose the weaknesses of state-of-the-art algorithms such as EAX\, LKH\, and NLKH.</s pan></p> END:VEVENT END:VCALENDAR