Ken Ohara

Osaka Prefecture University, Sakai, Ōsaka, Japan

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Publications (8)0 Total impact

  • Ken Ohara · Yusuke Nojima · Hisao Ishibuchi
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    ABSTRACT: We propose a route guidance method for guiding automobiles in road traffic systems, which is based on traffic information sharing through inter-vehicle communication (IVC). The proposed method does not require a centralized traffic information system. That is, a huge infrastructure is not required in the proposed method. Each driver, however, can utilize the latest traffic information using IVC. Through computational experiments, we show that good results are obtained from traffic information sharing when the percentage of drivers with the proposed method is low. When the percentage of such drivers is high, good results are not obtained because the same traffic information is shared by many drivers. In this case, many drivers tend to choose the same route, which degrades the overall traffic flow.
    No preview · Conference Paper · Nov 2007
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    ABSTRACT: We have examined the effect of spatial structures on the evolution of iterated prisoner's dilemma (IPD) game strategies. In our former study, we used two neighborhood structures, which follow the concept of structured demes. One is for the interaction among players through the IPD game. A player in each cell in a grid-world plays against its neighbors defined by this neighborhood structure. The other is for the mating of strategies by genetic operations. A new strategy for a player is generated by genetic operations from a pair of parent strings, which are selected from its neighbors defined by the second neighborhood structure. In this paper, we extend our IPD game simulation to a more realistic problem while keeping the simplicity of the original IPD game. We employ a stochastic strategy represented by a string of real numbers between 0 and 1. Each real number in the string denotes the probability of cooperation. We examine the effects of spatial structures on the evolution of IPD game strategies with probabilistic decision making in various payoff matrices. From simulation results, it is shown that cooperative behavior is evolved only when the interaction neighborhood is small and the mating neighborhood is also small for some payoff matrices.
    Preview · Conference Paper · Oct 2007
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    ABSTRACT: In the design of evolutionary multiobjective optimization (EMO) algorithms, it is important to strike a balance between diversity and convergence. Traditional mask-based crossover operators for binary strings (e.g., one-point and uniform) tend to decrease the diversity of solutions in EMO algorithms while they improve the convergence to the Pareto front. This is because such a crossover operator, which is called geometric crossover, always generates an offspring in the segment between its two parents under the Hamming distance in the genotype space. That is, the sum of the distances from the generated offspring to its two parents is always equal to the distance between the parents. In this paper, first we propose a non-geometric binary crossover operator to generate an offspring outside the segment between its parents. Next we examine the effect of the use of non-geometric binary crossover on single-objective genetic algorithms. Experimental results show that non-geometric binary crossover improves their search ability. Then we examine its effect on EMO algorithms. Experimental results show that non-geometric binary crossover drastically increases the diversity of solutions while it slightly degrades their convergence to the Pareto front. As a result, some performance measures such as hypervolume are clearly improved.
    Preview · Conference Paper · Jan 2007
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    ABSTRACT: We examine the effect of spatial structures on the evolution of iterated prisoner's dilemma (IPD) game strategies through computational experiments in single-dimensional and two-dimensional grid-worlds. Our computational experiments have two characteristic features. One is the use of a random pairing scheme in the IPD game where each player plays against a different randomly chosen opponent at every round of the dilemma game. The random pairing scheme makes it very difficult for players to evolve cooperative behavior. The other characteristic feature is the use of two neighborhood structures, which follows the concept of structured demes. One is for the interaction among players through the IPD game. A player in each cell in a grid-world plays against its neighbors defined by this neighborhood structure. The other is for the mating of strategies by genetic operations. A new strategy for a player is generated by genetic operations from a pair of parent strings, which are selected from its neighbors defined by the second neighborhood structure. It is shown that cooperative behavior is evolved only when the interaction neighborhood is very small and the mating neighborhood is small.
    No preview · Article · Jan 2006
  • Ken OHARA · Yusuke NOJIMA · Hisao ISHIBUCHI
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    ABSTRACT: 本研究では, 道路状況が動的に変化するために, 道路利用者が経路選択を行う時に個々の経路の走行時間に関する情報が利用可能でないような交通流モデルを考える. このような交通流モデルに対して, 全走行車両の平均走行時間を最小にするために, 2種類の経路選択手法の性能を比較する. 一つは, 大域的に平均走行時間の最小化を行う手法である. この手法では, 全走行車両の経路選択を中央管理者が決定する. 走行車両台数が多い場合では, 走行車両に対する経路選択の組合せ総数が大きくなり, 最適解を求めることが困難になる. そこで本論文では, 遺伝的アルゴリズムを用いることで効率的に近似最適解を求めることにする. もう一つの手法は, 個々の車両ごとに局所的に走行時間の最小化を行う手法である. この手法では, 各車両は, 予測走行時間の短い経路を選択する. 本論文では, ニューラルネットワークを用いて走行時間の予測を行う. 数値実験により, 2種類の経路選択手法を比較し, 各々の手法の特徴を明らかにする.
    No preview · Article · Jan 2006 · Journal of Japan Society for Fuzzy Theory and Intelligent Informatics
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    Ken Ohara · Yusuke Nojima · Hisao Ishibuchi
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    ABSTRACT: We consider a traffic flow model where the information about the actual travel time for each alternative route is not available when each driver performs route selection. For such a traffic flow model, we examine two routing methods to minimize the average travel time over all vehicles running in the model. One method tries to minimize the average travel time globally. It is assumed in this method that a central manager determines the routes of all vehicles. Since the number of combinations of vehicles' routes exponentially increases as the number of vehicles increases, we need an efficient combinatorial optimization technique. In this paper, we employ a genetic algorithm to search for a near-optimal route combination for all vehicles. In the other method, each driver tries to minimize his/her own travel time locally with no central manager. It is assumed in this method that each driver selects the route with the shortest estimated travel time among alternative routes. Each driver uses a neural network for the travel time estimation. Through computational experiments, we clearly demonstrate the characteristic features of each method.
    Preview · Article · Jan 2005
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    Hisao Ishibuchi · Ken Ohara · Yusuke Nojima
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    ABSTRACT: Elitism often has a large effect on the search ability of evolutionary algorithms. Many studies, however, did not discuss its implementation in cellular algorithms where a population of individuals is spatially distributed over a two-dimensional grid-world. In this paper, we examine two implementation schemes of elitism in cellular algorithms. One is global elitism where a number of the best individuals in the entire population are viewed as elite. The other is local elitism where an individual is viewed as elite when it is the best individual among its neighbors. Effects of elitism on the behavior of cellular algorithms are examined through performance evaluation, takeover time analysis, and diversity analysis. We use a cellular genetic algorithm with two neighborhood structures. One is for local competition among neighbors (e.g., fight for water and sunlight in the case of biological evolution of plants). The definition of local elitism is based on this competition neighborhood. The other is for local selection of parents from neighboring individuals. Since we have these two different neighborhood structures, we can specify the size of local competition for elitism independently of the size of local selection. Experimental results show that the choice of an implementation scheme of elitism has a dominant effect on the performance of our cellular genetic algorithm while it has only a slight effect on the takeover time. Good results are obtained under local elitism when the selection neighborhood is larger than the competition neighborhood. This relation in the size of the neighborhood structures
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    ABSTRACT: Inter-Vehicle Communication (IVC) is a promising technology for the next-generation of auto industry. In this paper, we examine the effectiveness of an IVC application for route selection. First we develop a traffic simulator based on a microscopic traffic flow model using cellular automata. Our simulation environment is a simple two-way road with traffic signals. We implement four route selection methods. They are an IVC-based route selection method, a Memory-based route selection method without traffic information, a centralized traffic information-based route selection method like Vehicle Information and Communication System (VICS), and a random route selection method. Next we examine the effectiveness of each method under various settings. Simulation results show that traffic information plays an important role in traffic congestion avoidance. The traffic, however, becomes heavier as the number of vehicles using the centralized traffic information increases. On the other hand, the traffic flow becomes smoother as the number of vehicles using IVC increases. Simulation results also show that the use of IVC does not cause the disturbance of the whole traffic flow. We show that IVC has a possibility of resolving the difficulties of VICS and social traffic problems.
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