report on ieee cis distinguished lecture program held …rcciit.org/docs/activity/a160218.pdf ·...

Report on IEEE CIS Distinguished Lecture Program held on 19th

November 2015 in the Department of Information Technology, RCC

Institute of Information Technology, Kolkata

The Department of Information Technology, RCC Institute of Information Technology,

Kolkata organized the IEEE CIS Distinguished Lecture Program on November 19th 2015 in

association with IEEE CIS Kolkata Chapter. Prof. Hisao Ishibuchi of Osaka Prefecture

University, Japan was the Distinguished Lecturer for the event.

The lecture started at Srinivas Ramanujam Auditorium of IT Department from 11:00 A.M.

Dr. Siddhartha Bhattacharyya, HoD (IT) and Dean (R & D), RCCIIT, Kolkata introduced to

the audience Prof. Hisao Ishibuchi. The audience comprised about 60 faculty members from

RCCIIT and other institutions. Prof. Arup K. Bhaumik, Principal, RCCIIT, felicitated Prof.

Ishibuchi with a bouquet of flowers.

The lecture was an enlightening one based on Evolutionary Many-Objective Optimization:

Search Behavior, Performance Indicators and Test Problems. It spanned across nearly 2

hours.

At the end of the lecture, Prof. Hisao Ishibuchi was handed over a token memento by Prof.

Arup K. Bhaumik. The lecture ended with a note of thanks to Prof. Hisao Ishibuchi by Dr.

Siddhartha Bhattacharyya.

Abstract of the lecture:

Evolutionary multi-objective optimization (EMO) has been an active research area in the

field of evolutionary computation for two decades. Recently various evolutionary many-

objective algorithms have been proposed in the literature. This is because many-objective

problems are difficult for frequently-used EMO algorithms such as NSGA-II and SPEA2.

This talk starts with a brief introduction to evolutionary computation. Next, some basic

concepts such as Pareto dominance, Pareto optimal solutions and Pareto front are explained

as an introduction to multi-objective optimization. After a basic framework of Pareto

dominance-based EMO algorithms is explained, co-evolutionary developments of test

problems and EMO algorithms are briefly explained. Then difficulties of many-objective

optimization are explained together with performance comparison results of some

representative EMO algorithms such as NSGA-II, MOEA/D, SMS-EMOA and HypE on

multi-objective and many-objective knapsack problems. It is also explained through

computational experimental results and simple numerical examples that performance

comparison results strongly depends on the specification of a reference point for the

hypervolume and a reference point set for the IGD indicator. Finally, solution set selection is

explained as a future research topics.

Few Glimpses of the Program