IEEE Kansai Section 第60回技術講演会

IEEE 関西支部会員各位
IEEE Kansai Section Members

IEEE Kansai Section
Chair Yukihiro Nakamura
中村 行宏

IEEE Kansai Section第60回技術講演会のご案内
The 60th IEEE Kansai Section Lecture Meeting

第60回は,大阪府立大学計算知能研究所殿のご協力のもと,Yew Soon Ong 様 (Nanyang Technological University) と Kay Chen Tan 様(National University of Singapore) をお招きし,下記のとおりに開催致します。


IEEE Kansai Section will have the 60th IEEE Kansai Section Lecture Meeting as follows.



Recent Topics in Evolutionary Computation


・Yew Soon Ong 氏
Nanyang Technological University
Memetic Computing誌 編集委員長
・Kay Chen Tan 氏
National University of Singapore
IEEE CI Magazine誌 編集委員長

日時(Time & Date)

2010年7月29日(木) 9:30‐11:30
Thursday, July 29, 2010, 9:30‐11:30


大阪府立大学 中百舌鳥キャンパス A14棟316会議室






講演1 "Towards Memetic Computing"
        Yew Soon Ong 氏(NTU,Memetic Computing誌 編集委員長)
講演2 "Advances in Evolutionary Multi-objective Optimization"
        Kay Chen Tan 氏 (NUS,IEEE CI Magazine誌 編集委員長)


・Yew Soon Ong 氏 (NTU,Memetic Computing 誌 編集委員長)
 "Towards Memetic Computing"
Memetic computation is a paradigm that uses the notion of meme(s) as units of information encoded in computational representations for the purpose of problem-solving. It covers a plethora of potentially rich meme-inspired computing methodologies, frameworks and operational algorithms including simple hybrids, adaptive hybrids and memetic automaton. In this talk, a brief insight to the multi-facet view of meme in memetic computation will be provided. The design issues anchoring on hybridization aspects of memetic computation is also reviewed. Subsequently, the talk takes focus on the algorithmic aspects and properties of a recently proposed Probabilistic Memetic Framework that formalizes the design of adaptive hybrids or adaptive memetic algorithm as opposed to the typical ad-hoc means of design. Finally, we take a peek into the journey towards memetic computing and gain some insights into agent computing framework as the key towards more enriching memetic computation research.

・Kay Chen Tan 氏 (NUS,IEEE CI Magazine 誌 編集委員長)
  "Advances in Evolutionary Multi-objective Optimization"
Evolutionary algorithms are stochastic search methods that are efficient and effective for solving sophisticated multi-objective (MO) problems. Advances made in the field of evolutionary multi-objective optimization (EMO) are the results of two decades worth of intense research, studying various topics that are unique to MO optimization. However many of these studies assume that the problem is deterministic, and the EMO performance generally deteriorates in the presence of uncertainties. In certain situations, the solutions found may not even be implementable in practice. In this talk, the challenges faced in handling different forms of uncertainties in EMO will be discussed. Specifically, the impact of these uncertainties on MO optimization will be described and the approaches/modifications to basic algorithm design for better EMO performance will be presented.


・Yew Soon Ong 氏(NTU,Memetic Computing誌 編集委員長)
Ph.D. from University of Southampton, United Kingdom 2002 Director of the Centre for Computational Intelligence, NTU Editor-in-Chief of Memetic Computing Journal Chief Editor of Book Series on Studies in Adaptation, Learning, and Optimization

・Kay Chen Tan 氏 (NUS,IEEE CI Magazine誌 編集委員長)
Ph.D. from the University of Glasgow, United Kingdom (1997) Editor-in-Chief of the IEEE Computational Intelligence Magazine General Chair of IEEE CEC 2007

参加費 (Fee)

無料 (Free)

参加申込み先 (Contact for registration)

小坂 有香子 Yukako Kosaka (NTT Communication Science Laboratories)
NTT コミュニケーション科学基礎研究所
Tel: (0774) 93 5518   Fax: (0774) 93 5158
E-mail: ieee-kansaiアット
会場準備の都合上,参加ご希望の方は7月27日(火)までに,Email(Faxでも結構 です)にて以下をお知らせください。

  • ご所属
  • お名前(ふりがな)
  • IEEE会員番号(会員の場合)
Please register in advance. E-mail or fax, IEEE member ID (if you have), your name and affiliation by July 27, 2010 (Tuesday).

本件連絡先 (For further information)

IEEE Kansai Section Technical Program Committee Chair
山田 武士(NTT先端技術総合研究所)


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