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  • 計算
    制御調査分科会
    日本機械学会誌
    1966年 69 巻 569 号 771-778
    発行日: 1966/06/05
    公開日: 2017/06/21
    解説誌・一般情報誌 フリー
  • 計算
    機世代人
    林業経済
    1990年 43 巻 10 号 i
    発行日: 1990/10/20
    公開日: 2018/01/22
    ジャーナル フリー
  • カシオ
    計算
    機(株)
    日本音響学会誌
    1985年 41 巻 6 号 419-420
    発行日: 1985/06/01
    公開日: 2017/06/02
    ジャーナル フリー
  • 開発 拓也, 渡邉 真也
    進化
    計算
    学会論文誌

    2019年 9 巻 3 号 93-102
    発行日: 2019年
    公開日: 2019/01/25
    ジャーナル フリー

    In the real world, it has been strongly desired to develop an algorithm for solving an optimization problem with many variables under strictly limiting the number of function calls. Because of this kind of reason, evolutionary computation competition 2017 in evolutionary computation symposium hosted by the JSEC was designed for enhancing the development of practical optimization algorithms. The main features of this competition are that the benchmark problem is "Benchmark Problem Based on Real-World Car Structure Design Optimization(Mazda Benchmark Problem)" created from the actual real problem in the car company and the computational condition for optimizing this problem is so strict. In this competition, the number of function calls is limited to only 30,000 even though the number of variables of this problem is over 200 and the landscape of this problem is multi-modal. This paper presents the winning algorithm of this competition in the single-objective category and tries to reveal the reasons why this algorithm could work so effectively in competition problem. This algorithm is based on estimating a high potential search area by iterating solution sampling like Estimation of Distribution Algorithm (EDA) and has a mechanism for improving the algorithm's efficiency. The most important points of this algorithm are very simple and with no unnecessary mechanisms. Through applying this algorithm to not only the competition benchmark problem but also some typical test problems, the effectiveness of this algorithm was confirmed and the characteristics of this algorithm were analyzed.

  • 大山 聖
    進化
    計算
    学会論文誌

    2018年 9 巻 2 号 86-92
    発行日: 2018年
    公開日: 2018/10/10
    ジャーナル フリー

    Evolutionary computation competition 2017 was held in December 9, 2017 in conjunction with evolutionary computation symposium 2017. It was confirmed that evolutionary algorithms can discover good designs of the design optimization problem of vehicle structures provided by Mazda motor company. Nine teams participated in the single-objective optimization division and eleven teams in the multiobjective optimization division. Prof. Shinya Watanabe's team from Muroran Institute of Technology won in the single-objective optimization division, Prof. Isao Ono's team from Tokyo Institute of Technology won in the multi-objective optimization division. The industrial use special prize was awarded to Dr. Tomohiro Harada's team from Ritsumeikan University. In the single-objective design optimization division, the groups using evolution strategies found good Pareto-optimal solutions. In the multiobjective optimization division, the groups who found good Pareto-optimal designs studied characteristics of the benchmark problem very much and implemented the most suitable optimization algorithm. Mazda benchmark problem has many severe constraints and thus feasible design space is strictly limited. Some teams used special techniques such as ε constraint method. Current result indicated that balance between search in feasible region and infeasible region may be important for constrained design optimization problems.

  • 注意課題時におけるfNIRS データへの適用
    原田 圭, 廣安 知之, 日和 悟
    進化
    計算
    学会論文誌

    2018年 9 巻 2 号 75-85
    発行日: 2018年
    公開日: 2018/10/10
    ジャーナル フリー

    MOEA/D decomposes a multiobjective optimization problem into a set of single objective subproblems. When there are a few differences in difficulty of each objective function, it can obtain widely-spread and uniformly-distributed solutions. However, in real-world problems, the complexities of the objective functions are often heterogeneous. In this case, each subproblem of the MOEA/D has different difficulty so that the spread and uniformity of the population is deteriorated because the search direction in the objective space tends to be biased into the feasible region which is easily explored. To overcome this issue, an adaptive weight assignment strategy for MOEA/D is proposed in this paper. In the proposed method, the subproblems are divided into some groups and the convergence speed is estimated for each group and utilized as the metric of the difficulty of the subproblems. Moreover, the weight vectors of easy subproblem groups are modified to bias their search into the subproblem group with higher difficulty. Our proposed method is validated on the region-of-interests determination problem in brain network analysis whose objective functions have heterogeneous difficulties. The experimental results showed that our method worked better than the conventional weight assignment strategy in MOEA/D.

  • 藤川 貴弘, 照井 勇輔, 渡邉 真也, 米本 浩一
    進化
    計算
    学会論文誌

    2018年 9 巻 2 号 61-74
    発行日: 2018年
    公開日: 2018/11/08
    ジャーナル フリー

    Linear aerospike engine is a rocket engine that is composed of arrays of small cell engines and a large spike nozzle whose one side is open to atmosphere. It is one of the promising propulsion systems for future space transportation since it can realize high performance for a wide range of ambient pressure conditions. Despite this advantage of the aerospike engine, previous design studies on aerospike nozzle shape are only devoted to maximizing the performance at a single design point. In order to explore the design of the aerospike engine considering performance at multiple operating altitudes, multi-objective design optimization is conducted in this paper. Design variables define cell engine parameters and the shape of the spike nozzle whose parameterization is carried out using monotonic cubic spline. An engineering-level performance analysis model of the engine is developed by combining 1) chemical equilibrium calculation for cell engines, 2) Riemann solver for spike wall flow, and 3) theoretical model for spike base flow. Five objective functions are considered for the maximization of specific impulse at three operating altitudes, the minimization of spike nozzle arc length, and the minimization of total engine height. The formulated many-objective problem is solved via MOEA/D with dynamic control of aggregate functions, and well-converged and widely-spread nondominated solutions are obtained. In these solutions, spike nozzle shapes that are different from shapes designed by previous methods are observed. After representative solutions are inspected in detail, the relations between objective functions and design variables in superior solutions are revealed using parallel coordinates plots.

  • 浅井 康平, 榊原 一紀, 中村 正樹
    進化
    計算
    学会論文誌

    2018年 9 巻 2 号 53-60
    発行日: 2018年
    公開日: 2018/10/18
    ジャーナル フリー

    We propose a sensitivity analysis technique for the class of mathematical programming problems. So far, there are no concrete methodologies of sensitivity analysis for the mathematical programming problems especially with integer constraints in general. Quantifier elimination is a concept of simplification used in mathematical logic and enables problems to be analyzed of their sensitivities to the objective functions. In this paper, we applied the quantifier elimination to a class of job shop scheduling problems as a case of mixed integer programming problems in order to demonstrate the evaluation of the sensitivities of the processing time to both of the makespan and the due date tardiness. In order to cope with computational complexities of quantifier elimination, we propose the problem decomposition and the sequential application of the quantifier elimination techniques based on the decomposition.

  • 大伴 周也, 原田 智広, ターウォンマット ラック
    進化
    計算
    学会論文誌

    2018年 9 巻 2 号 41-52
    発行日: 2018年
    公開日: 2018/08/07
    ジャーナル フリー

    This paper proposes an effective algorithm for the recently proposed simultaneous design optimization problem of multiple car structures. In recent years, evolutionary algorithms typified by genetic algorithms have been extensively studied to solve single- and multi-objective real-world optimization problems. Mazda Motor Corporation developed the simultaneous design optimization benchmark problem that is based on a real car structures design and consists of many design variables and severe constraints. In this benchmark, three models of cars are simultaneously optimized and it is difficult to acquire optimal solutions with the limited number of evaluations with existing methods. This paper aims at proposing an algorithm based on NSGA-II, one of the most typical multi-objective evolutionary algorithm, and introduces several modifications considering the characteristics of the Mazda's benchmark problem. Specifically, we propose a method to effectively generate parent individuals using the characteristic that design variables of three cars are independent and genetic manipulation taking into consideration the characteristics of the objective function. In order to verify the effectiveness of the proposed method, we conduct experiments using the Mazda's benchmark problem. In the experiment, we compare NSGA-II with the proposed modifications with the original NSGA-II. The experimental result reveals that the proposed method can acquire extremely better solution set compared with the existing method.

  • 鳥山 直樹, 小野 景子
    進化
    計算
    学会論文誌

    2018年 9 巻 2 号 32-40
    発行日: 2018年
    公開日: 2018/06/01
    ジャーナル フリー
    In this paper, we present an efficient sampling method for a multimodal and high-dimensional distribution. For sampling from a high-dimensional distribution, DE-MC, which is based on the Markov chain Monte Carlo(MCMC) methods, has been proposed. It showed good performance in sampling from any probability distribution based on constructing a Markov chain that has the desired distribution. However, DE-MC has inherent difficulties in sampling from a multimodal distribution. To overcome this problem, we incorporate a replica exchange method into DE-MC and propose a replica exchange resampling DE-MC method (reRDE-MC) based on sampling importance resampling to improve its performance. The proposed method is evaluated by using three types of distributions with multimodal and high dimensions as artificial data. We verified that the proposed method can sample from a multimodal and highdimensional distribution more effectively than by a conventional method. We then evaluated the proposed method by using financial data as actual data, and confirmed that the proposed method can capture the behavior of financial data.
  • 串田 淳一, 佐藤 寛之, 渡邉 真也, 能島 裕介
    進化
    計算
    学会論文誌

    2018年 9 巻 2 号 31
    発行日: 2018年
    公開日: 2019/03/29
    ジャーナル フリー
  • 阪本 直気, 秋本 洋平
    進化
    計算
    学会論文誌

    2018年 9 巻 1 号 21-30
    発行日: 2018年
    公開日: 2018/06/01
    ジャーナル フリー
    In this paper we focus on linearly constrained black-box optimization problems. We consider to use the covariance matrix adaptation evolution strategy (CMA-ES) to solve linearly constrained problems. A goodness of the CMA-ES is its invariant properties: invariance to increasing transformation of the objective function and invariance to affine transformation of the search space coordinate. These peroperties make the CMA-ES a state-of-the-art search algorithm for ill-conditioned and nonseparable unconstrained problems. When it is applied to a constrained problem, however, it is coupled with a constraint handling method that often breaks the invariance properties that the CMA-ES exhibits. To fully exploit the performance of the CMA-ES in constrained problems, a constraint handling method needs to have all the following invariance: invariance to arbitrary element-wise increasing transformation of the objective and constraint functions, invariance to affine transformation of the search space, and invariance to redundant constraints. In this paper, we propose a novel linear constraint handling technique with the above-mentioned invariance properties for the CMA-ES. The proposed method virtually transforms a constrained problem into an unconstrained one by adaptive penalization. The penalized fitness is defined as a weighted sum of the ranking on the objective and the ranking on the constraint violations measured by the Mahalanobis distance between each candidate solution to its projection onto the boundary of the constraints. Experimental results show that the CMA-ES with the proposed constraint handling exhibits the above-mentioned invariance properties and performs similarly both on constrained problems and their unconstrained counterpart.
  • Kui Chen, Hitoshi Kanoh
    進化
    計算
    学会論文誌

    2019年 9 巻 3 号 103-114
    発行日: 2019年
    公開日: 2019/02/09
    ジャーナル フリー

    Recently, some discrete swarm intelligence algorithms such as particle swarm optimization with hamming distance (HDPSO), similarity artificial bee colony (S-ABC), and discrete firefly algorithm (DFA) have been proposed to solve graph 3-coloring problems (3-GCP) and obtain good results. However, these algorithms use static parameter settings that limit their performance on graphs with various sizes and topology. In this paper, we propose a discrete adaptive artificial bee colony (A-ABC) algorithm that can adjust the parameter automatically during the evolution according to the graph size and the fitness of candidates. For the convenience of comparison, we also propose a fixed ABC (F-ABC), which is identical to A-ABC but using fixed parameter setting during the evolution. A-ABC is simple and high performance. Experiments on 3-GCP show that A-ABC dramatically outperforms its competitors F-ABC, HDPSO, S-ABC, and DFA. We also study the scout bee phase and report that the scout bee phase is not required in solving 3-GCP

  • 大橋 響太郎, 藤吉 夏生, 秋本 洋平
    進化
    計算
    学会論文誌

    2018年 9 巻 1 号 10-20
    発行日: 2018年
    公開日: 2018/04/18
    ジャーナル フリー
    In this paper, we describe the IBP+EC which is a Reinforcement Learning (RL) method combining the Instance-Based Policy (IBP) and optimization by Evolution Computation (EC). The IBP+EC has attracted attention as a promising Direct Policy Search for severe problems that required foreseeing, and switching of control law according to a situation. However, the IBP has strong redundancy due to its policy expression, which makes optimization difficult. The main causes of the redundancy are "dependencies between instances" and "inactive instances". In particular, the "inactive instance" is a peculiar problem of the IBP and it is difficult to handle with a general-purpose optimization algorithm. Besides, the IBP needs to tune the number of instances, which is a parameter that significantly affects its performance. In a general control problem, since the optimal number of instances is rarely found out, its tuning is complicated. In this paper, we propose the IBP-CMA as an optimization method specialized for IBP-Optimization to cope with the above difficulties. The IBP-CMA algorithm is based on the (1+1)-CMA-ES which is a general evolutionary computation method for continuous optimization and is an elitist strategy. By the nature of (1+1)-CMA-ES, the IBP-CMA deals with the "dependencies between instances". Moreover, the IBP-CMA copes with the above-mentioned peculiar problem of the IBP by combining the (1+1)-CMA-ES with the mechanism that activates inactive instances and adapts the number of instances. The experiments in RL tasks show that the IBP-CMA performs well without parameter pre-adjustment.
  • 趙 冬青, アランニャ クラウス, 狩野 均
    進化
    計算
    学会論文誌

    2018年 9 巻 1 号 1-9
    発行日: 2018年
    公開日: 2018/04/20
    ジャーナル フリー
    The facility layout problem (FLP) is a configuration problem where the goal is to determine the most efficient arrangement of interacting departments in a facility so as to optimize the working time or material cost. The departments are usually represented as rectangle objects which location and dimensions that should be optimized. Since the problem is quite complex and very difficult to solve, approximate approaches for this problem have been popular in recent years.In this paper, our goal is to determine the positions and aspect ratios of a number of irregular rectangles so that the transportation cost is minimized. We propose a method using the evolutionary algorithm (EA) based on Levy Flights (LFs) for this problem. The proposed method uses LF to search a new solution on the search space. Recently, methods based on LF have also been shown to be more effective than the based on Random Walk. Since the main operation of EA is mutation, we expect an EA-LF combination to perform well. Experiments on the benchmark problems with the number of departments from 7 to 62 indicate that the proposed method is effective for large scale problems than conventional methods such as genetic algorithms and ant colony optimization.
  • 近藤 俊樹, 立川 智章
    進化
    計算
    学会論文誌

    2018年 8 巻 3 号 88-99
    発行日: 2018年
    公開日: 2018/02/10
    ジャーナル フリー
    In this paper, we propose a new adaptive discretization method of design variables on real-coded genetic algorithms(RCGAs) for improving convergence performance while maintaining diversity.The convergence can be accelerated by setting the appropriate number of discrete classes in RCGAs. However, it is difficult to decide it in advance in most of the practical optimization problems.In addition, the diversity may be lost if the number of discrete classes is too small.In order to overcome these difficulties, we use a simple index which is based on the standard deviation to adaptively determine the number of discrete classes in each design variable.Since the proposed method merely rounds the value of the design variable after applying genetic operators such as crossover and mutation, it can be applied to various RCGAs.Here, we use NSGA-II as an RCGA and investigate the performance efficiency of convergence and diversity by using nineteen benchmark problems, including engineering problems.The convergence and diversity performance are evaluated using GD and IGD, respectively.The results of the numerical experiments show that the proposed method can obtain good convergence while maintaining diversity.
  • 賀川 祐太朗, 渡邉 真也, 金崎 雅博, 依田 英之, 千葉 一永
    進化
    計算
    学会論文誌

    2018年 8 巻 2 号 75-87
    発行日: 2018年
    公開日: 2018/02/10
    ジャーナル フリー
    In recent years, there has been many examples of applying evolutionary multi-criterion optimization (EMO) to practical problems in many fields. On the other hand, a new problem of how to analyze non-dominated solutions (NDSs) with many design variables and many objectives arises. For this problem, we has provided our original analysis support system using association rules, which is correlation-based information hierarchical structuring method (CIHSM). CIHSM could extract features of NDSs through objective analysis using association rules and visually present result of analyses as a hierarchical tree. However, there remains two problems in our CIHSM; the parameter setting related to association rules and the feature extraction required by user's interest. In this paper, we have proposed a modified CIHSM having two mechanism for dissolving these two problems. We called it ``on-demand CIHSM''. The first mechanism is the feature selection according to user's interest region in objective space. The important point of this mechanism is that user can select his interests region visually. And the second mechanism is to tune the value of minimum support parameter automatically. The setting of this parameter has a strong influence for the number of extracted rules. But this mechanism could provide use's requirement number of rules without tuning the value of this parameter. To investigate the effectiveness of on-demand CIHSM, we applied it to the conceptual design problem of hybrid rocket engine(HRE) problem, which is a real problem provided by JAXA. Through this experiments, it was verified that our on-demand CIHSM is very useful to extract features of NDS according to user's interest.
  • 西田 昂平, 秋本 洋平
    進化
    計算
    学会論文誌

    2017年 8 巻 2 号 61-74
    発行日: 2017年
    公開日: 2017/12/01
    ジャーナル フリー
    The population size, i.e., the number of candidate solutions per iteration, is the only parameter for the covariance matrix adaptation evolution strategy (CMA-ES) that needs to be tuned depending on the ruggedness and the uncertainty of the objective function. The population size has a great impact on the performance of the CMA-ES, however, it is prohibitively expensive in black-box scenario to tune the population size in advance. Moreover, a reasonable population size is not constant during the optimization. In this paper, we propose a novel strategy to adapt the population size. We introduce the evolution path in the parameter space of the Gaussian distribution, which accumulates successive parameter updates. Based on the length of the evolution path with respect to the Fisher metric, we quantify the accuracy of the parameter update. The population size is then updated so that the quantified accuracy is kept in the constant range during search. The proposed strategy is evaluated on test functions including rugged functions and noisy functions where a larger population size is known to help to find a better solution. The experimental results show that the population size is kept as small as the default population size on unimodal functions, and it is increased at the early stage of the optimization of multimodal functions and decreased after the sampling distribution is concentrated in a single valley of a local optimum. On noisy test functions, the proposed strategy start increasing the population size when the noise-to-signal ratio becomes relatively high. The proposed strategy is compared with the CMA-ES and the state-of-the-art uncertainty handling in the CMA-ES, namely UH-CMA-ES, with a hand-tuned population sizes.
  • 長谷川 拓, 森 直樹, 松本 啓之亮
    進化
    計算
    学会論文誌

    2018年 8 巻 2 号 52-60
    発行日: 2018年
    公開日: 2018/02/11
    ジャーナル フリー
    In Evolutionary Computation (EC), it is difficult to maintain efficient building blocks and to combine them efficiently. In particular, the control of building blocks in the population of Genetic Programming (GP) is relatively difficult because of tree-shaped individuals and also because of bloat, which is the uncontrolled growth of ineffective code segments in GP. It has been reported that the parameter tuning for solving the above mentioned problems requires a significant amount of efforts. Aimed at utilizing building blocks efficiently, this paper presents a GP algorithm called “Genetic Programming with Multi-Layered Population Structure (MLPS-GP)”. MLPS-GP employs multi-layered population like the pyramid-like population and searches solutions using local search and crossover. The computational experiments were conducted by taking several classical Boolean problems as examples.
  • 渡辺 哲朗, 菅野 太郎, 古田 一雄
    進化
    計算
    学会論文誌

    2017年 8 巻 2 号 36-51
    発行日: 2017年
    公開日: 2017/11/22
    ジャーナル フリー
    In recent years, accidents and product recalls caused by product failures have become major problems in many industries worldwide. To predict how changes of a product recall system affects safety in the society and to get valuable suggestions to improve product recall systems, we simulated the recall process in society using social simulation model. This research is important because the current product recall systems are not designed by mathematical and predictive approaches such as a computer simulation, but designed by empirical approaches. As a simulation model, we propose Layered Co-evolution Model with Logic Value Typed Genetic Programming (GP). We evaluated the proposed method by using the multi-agent simulation in an artificial society where producer agents and consumer agents both compete and cooperate with each other. This experiment discovered that the producer agents and the consumer agents are able to co-evolve toward a convergence point in Layered Co-evolution Model through the interactions between both types of agents. From the experiment, it is also understood that Logic Value Typed GP, which uses logic values and logic operators, has the advantages over the existing GP method that uses real number values. The Logic Value Typed GP is more stable in the evolutionary process and more efficient in terms of agents' learning process. In addition, we predicted that making the accident-compensation-level stricter decreases the frequency of product accidents in the whole artificial society. This is the result of the producer agents increasing the frequency of product recalls or raising production costs under such a stricter level. This prediction is useful for realizing a safer society.
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