Research presentations
Number of the published data : 44
No. Assortment Subject of presentation Conference Date URL Description
1 Oral presentation(general)
差分進化における相関係数に基づく遺伝子のグループ化とグループ単位の交叉の提案
情報処理学会 第120回数理モデル化と問題解決研究会
2018/09/26

最適化が困難な変数間依存性の強い問題に出現する特徴的な分布として,細い楕円形状の分布がある.このような場合に優れた子個体を生成するには,楕円形状の長軸に沿って変数を同時に変更する必要がある.また,同様の分布は,変数分離型の問題において探索点集合が最適解から離れている場合にも出現する.差分進化における2項交叉は,各変数(遺伝子) について同じ確率で交叉を行うかどうかを決定しているため,特定の遺伝子を同時に交叉することは困難である.本研究では,このような形状を検出するために探索点の相関係数を利用する方法を提案する.探索点の分布から相関行列を求め,相関の強い遺伝子をグループ化し,グループ単位で遺伝子を同時に交叉する(あるいは,交叉しない).本手法を差分進化の代表的手法であるJADE に導入し,幾つかのベンチマーク問題を最適化し,性能を比較することにより,本手法の効果を調べる.
2 Oral presentation(general)
変数間依存性を解消する変換を導入したブレンド交叉の提案
京都大学数理解析研究所RIMS共同研究(公開型)「不確実性の下での意思決定理論とその応用 :計画数学の展開」
2017/11/15


3 Oral presentation(general)
Particle Swarm Optimization with the Velocity Updating Rule According to an Oblique Coordinate System
The 2nd International Symposium on Swarm Behavior and Bio-Inspired Robotics (SWARM2017)
2017/10/30


4 Oral presentation(general)
An Adaptive Differential Evolution with Exploitation and Exploration by Extreme Individuals
SICE Annual Conference 2017 (SICE2017)
2017/09/22


5 Oral presentation(general)
An Adaptive Differential Evolution with Learning Parameters According to Groups Defined by the Rank of Objective Values
The Eighth International Conference on Swarm Intelligence (ICSI2017)
2017/07/27


6 Oral presentation(general)
斜交座標系に基づく回転不変なブレンド交叉の提案
情報処理学会 第113回数理モデル化と問題解決研究会
2017/06/24


7 Oral presentation(general)
差分進化における個体のグループ化とグループ別パラメータ制御の効果
第10回コンピューテーショナル・インテリジェンス研究会 (pp.17-24)
2016/12/16


8 Oral presentation(general)
適応型差分進化JADE における個体順位に基づくグループ別パラメータ制御
京都大学数理解析研究所「確率的環境下における数理モデルの理論と応用 」研究集会
2016/11/11


9 Oral presentation(general)
An Adaptive Differential Evolution with Adaptive Archive Selection and Hill-Valley Detection
2016 IEEE Congress on Evolutionary Computation (CEC2016)
2016/07/25


10 Oral presentation(general)
Learning Game Players by an Evolutionary Approach Using Pairwise Comparison without Prior Knowledge
International Conference on Intelligent Informatics and BioMedical Sciences 2015 (ICIIBMS2015)
2015/11/28


11 Oral presentation(general)
Improving an Adaptive Differential Evolution Using Hill-Valley Detection
The 7th International Conference on Soft Computing and Pattern Recognition (SoCPaR2015)
2015/11/15


12 Oral presentation(general)
Improving Particle Swarm Optimization by Estimating Landscape Modality Using a Proximity Graph
The First International Symposium on Swarm Behavior and Bio-Inspired Robotics (SWARM2015)
2015/10/30


13 Oral presentation(general)
Emerging Collective Intelligence in Othello Players Evolved by Differential Evolution
2015 IEEE Conference on Computational Intelligence and Games (CIG2015)
2015/09/01


14
An Adaptive Differential Evolution Considering Correlation of Two Algorithm Parameters
The Joint 7th International Conference on Soft Computing and Intelligent Systems and 15th International Symposium on Advanced Intelligent Systems (SCIS&ISIS 2014)
2014/12/04


15 Oral presentation(general)
Selecting Strategies in Particle Swarm Optimization by Sampling-Based Landscape Modality Detection using Inner Products
SICE Annual Conference 2014
2014/09/12


16 Oral presentation(general)
Selecting Strategies in Particle Swarm Optimization by Sampling-Based Landscape Modality Detection
The 2014 International Conference on Parallel and Distributed Processing Techniques and Applications
2014/07/21

If the landscape of the objective function is
unimodal, the efficiency of population-based optimization
algorithms (POAs) can be improved by selecting strategies
for local search around a best solution. If the landscape is
multimodal, the robustness of the POAs can be improved
by selecting strategies for global search in search space.
We have proposed a method that estimates the landscape
modality by sampling the objective values along a line and
counting the number of changes in the objective values
from increasing to decreasing and vice versa. In this study,
in order to improve the performance of particle swarm
optimization (PSO), we propose to select a proper strategy
according to the landscape modality: The gbest model is
selected in unimodal landscape and the lbest model is
selected in multimodal landscape. The advantage of the proposed method is shown by solving unimodal and multimodal
problems and by comparing it with standard PSOs.
17 Oral presentation(general)
進化的アルゴリズムにおける関数形状の概形推定
第3回コンピューテーショナル・インテリジェンス研究会
2013/08/30


18 Oral presentation(general)
Differential Evolution and Its Advancement
2013 IEEE SMC Hiroshima Chapter 若手研究会
2013/07/13


19 Oral presentation(general)
Efficient Constrained Optimization by the Epsilon Constrained Differential Evolution with Rough Approximation Using Kernel Regression
2013 IEEE Congress on Evolutionary Computation
2013/06/22


20 Oral presentation(general)
競合ヘブ則によるグラフ生成に基づく種分化型Differential Evolution の提案
第22回インテリジェント・システム・シンポジウム
2012/08/31


21 Oral presentation(general)
Differential Evolution with Graph-Based Speciation by Competitive Hebbian Rules
The Sixth International Conference on Genetic and Evolutionary Computing
2012/08/26


22 Oral presentation(general)
Large Scale Optimization by Differential Evolution
with Landscape Modality Detection and a Diversity Archive
2012 IEEE Congress on Evolutionary Computation
2012/06/14

In this study, the performance of Differential Evolution
(DE) with landscape modality detection and a diversity archive (LMDEa) is reported on the set of benchmark functions provided for the CEC2012 Special Session on Large Scale Global Optimization. In DE, large population size which exceeds the problem dimensions largely, is adopted to keep the diversity of search. However, it is difficult to adopt such large size to solve large scaled optimization problems. In this study, we propose to solve large scale optimization problems using small population size and a large archive for diversity.
Also, we propose simple control of scaling factor by observing landscape modality of search points in order to keep diversity. In LMDEa, some points on a line connecting the centroid of search points and a search point are sampled. When the objective values of the sampled points are changed decreasingly and then increasingly, it is thought that one valley exists. If there exists only one valley, the landscape is unimodal and small scaling factor is adopted. Otherwise, large scaling factor is adopted. The effect of the proposed method is shown by solving the benchmark functions.
23 Poster presentation
Differential Evolution with Dynamic Strategy and Parameter Selection by Detecting Landscape Modality
2012 IEEE Congress on Evolutionary Computation
2012/06/13

Differential Evolution (DE) is an evolutionary algorithm. DE has been successfully applied to optimization problems including non-linear, non-differentiable, non-convex and multimodal functions. There are several mutation strategies
such as the best and the rand strategy in DE. It is known
that the best strategy is suitable for unimodal problems and the rand strategy is suitable for multimodal problems. However, the landscape of a problem to be optimized is often unknown and the landscape is changing dynamically while the search process proceeds. In this study, we propose a new and simple method that detects the modality of landscape being searched: unimodal or not unimodal. In the method, some points on the line connecting the centroid of search points and the best search point are sampled.
When the objective values of the sampled points are changed
decreasingly and then increasingly, it is thought that one valley exists. If there exists only one valley, the landscape is unimodal and a greedy strategy like the best strategy is adopted. Otherwise, the rand strategy is adopted. The effect of the proposed method is shown by solving some benchmark problems.
24 Oral presentation(general)
Efficient Constrained Optimization by
the epsilon Constrained Rank-Based Differential Evolution
2012 IEEE Congress on Evolutionary Computation
2012/06/11

The epsilon constrained method is an algorithm transformation method, which can convert algorithms for unconstrained problems to algorithms for constrained problems using the epsilon level comparison, which compares search points based on the pair of objective value and constraint violation of them. We have proposed the epsilon constrained differential evolution eDE, which is the combination of the epsilon constrained method and differential evolution (DE). In this study, we propose the epsilon constrained rank-based DE (eRDE), which adopts a new and simple scheme of controlling algorithm parameters in DE. In the scheme, different parameter values are selected for each individual. Small scaling factor and large crossover
rate are selected for good individuals. Large scaling factor and small crossover rate are selected for bad individuals. The goodness is given by the ranking information. The eRDE can find high-quality solutions in very small number of function evaluations. It is shown that the eRDE can find near optimal solutions stably in about half the number of function evaluations compared with various other methods on well known nonlinear constrained problems.
25 Oral presentation(general)
Fuzzy C-Means Clustering and Partition Entropy for Species-Best Strategy and Search Mode Selection in Nonlinear Optimization by Differential Evolution
2011 IEEE International Conference on Fuzzy Systems
2011/06/28


26 Oral presentation(general)
Efficient Nonlinear Optimization by Differential Evolution with a Rotation-Invariant Local Sampling Operation
2011 IEEE Congress on Evolutionary Computation
2011/06/08

Differential Evolution (DE) is a newly proposed evolutionary algorithm. DE has been successfully applied to
optimization problems including non-linear, non-differentiable,
non-convex and multimodal functions. However, the performance of DE degrades in problems having strong dependence among variables, where variables are strongly related to each other.
One of the desirable properties of optimization algorithms for solving the problems with the strong dependence is rotation-invariant property. In DE, the mutation operation is rotation-invariant,
but the crossover operation is not rotation-invariant
usually. In this study, we propose a new operation, called local
sampling operation that is rotation-invariant. In the operation,
independent points are selected from the population, difference
vectors from a parent to the points span a local area centered
at the parent, and a new point is generated around the area.
Also, the operation is used for judging whether intensive search
or extensive search is desirable in each generation. The effect
of the proposed method is shown by solving some benchmark
problems.
27 Oral presentation(general)
Solving Nonlinear Optimization Problems by Differential Evolution with a Rotation-Invariant Crossover Operation using Gram-Schmidt process
World Congress on Nature and Biologically Inspired Computing (NaBIC2010)
2010/12


28 Oral presentation(general)
Constrained Optimization by the Epsilon Constrained Differential Evolution with an Archive and Gradient-Based Mutation
2010 IEEE Congress on Evolutionary Computation
2010/07


29 Oral presentation(general)
Efficient Constrained Optimization by the Epsilon Constrained Adaptive Differential Evolution
2010 IEEE Congress on Evolutionary Computation
2010/07


30 Oral presentation(general)
A Comparative Study on Kernel Smoothers in Differential Evolution with Estimated Comparison Method for Reducing Function Evaluations
2009 IEEE Congress on Evolutionary Computation
2009/05


31 Oral presentation(general)
低精度近似モデルを導入したDifferential Evolutionとε制約法による効率的制約付き最適化
進化計算シンポジウム2008
2008/12


32 Oral presentation(general)
Predicting Stock Price using Neural Networks Optimized by Differential Evolution with Degeneration
2008 International Symposium on Intelligent Informatics
2008/12


33 Oral presentation(general)
Efficient Optimization by Differential Evolution using Rough Approximation Model with Adaptive Control of Error Margin
Joint 4th International Conference on Soft Computing and Intelligent Systems and 9th International Symposium on advanced Intelligent Systems (SCIS&ISIS 2008)
2008/09


34 Oral presentation(general)
Reducing Function Evaluations in Differential Evolution using Rough Approximation-Based Comparison
2008 IEEE Congress on Evolutionary Computation
2008/06


35 Oral presentation(general)
Structural Learning of Neural Networks by Differential Evolution with Degeneration using Mappings
2007 IEEE Congress on Evolutionary
Computation
2007/09


36 Oral presentation(general)
Solving Nonlinear Constrained Optimization Problems by the ε Constrained Differential Evolution
2006 IEEE Conference on Systems, Man, and Cybernetics
2006/10


37 Oral presentation(general)
Constrained Optimization by the epsilon Constrained Differential Evolution with Gradient-Based Mutation and Feasible Elites
2006 IEEE Congress on Evolutionary Computation
2006/07


38 Oral presentation(general)
Solving Constrained Optimization Problems by the epsilon Constrained Particle Swarm Optimizer with Adaptive Velocity Limit Control
The 2nd IEEE International Conference on Cybernetics & Intelligent Systems
2006/06


39 Oral presentation(general)
Constrained Optimization by the epsilon Constrained Hybrid Algorithm of Particle Swarm Optimization and Genetic Algorithm
The 18th Australian Joint Conference on Artificial Intelligence
2005/12


40 Oral presentation(general)
Constrained Optimization by epsilon Constrained Particle Swarm Optimizer with ε-level Control
The 4th IEEE International Workshop on Soft Computing as Transdisciplinary Science and Technology (WSTST'05)
2005/05


41 Oral presentation(general)
Structural Learning of Neural Networks by Coevolutionary Genetic Algorithm with Degeneration
2004 IEEE International Conference on Systems, Man, and Cybernetics (SMC2004)
2004/10


42 Oral presentation(general)
Constrained Optimization by Combining the alpha Constrained Method with Particle Swarm Optimization
Joint 2nd International Conference on Soft Computing and Intelligent Systems and 5th International Symposium on Advanced Intelligent Systems (SCIS&ISIS2004)
2004/09


43 Oral presentation(general)
Structural Learning of RBF-Fuzzy Rule Bases Based on Information Criteria and Degeneration
2003 IEEE International Conference on Systems, Man, and Cybernetics (SMC2003)
2003/10


44 Oral presentation(general)
Learning Structure of RBF-Fuzzy Rule Bases by Degeneration
2003 International Conference on Fuzzy Information Processing
2003/03