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Emergence of adaptive behaviors for artificial creature using a combined artificial neural network

ARTIFICIAL LIFE AND ROBOTICS 2016年第1期21卷 91-97页

作者： Nakamura, Keita Gunma Natl Coll Technol Dept Informat & Comp Engn Maebashi Gunma Japan

This study proposes a method to acquire adaptive behavior for artificial creature which has a lot of joints using a combined Artificial Neural Network (ANN). Experiment in this study focuses on artificial fish model, which has a lot of joints, tracking towards a target in the virtual water environment. In order to control motions of joints, a combined ANN is implemented with the model. At first, one ANN is prepared to control specific joints so as to swim basically in response to minimal input information using evolutionary computation in preliminary experiments. And an new network is constructed by combining its network and the other network. In order to acquire complicate behavior for artificial creature, weights of combined ANN are optimized. Experiment result shows the model which has many joints acquire adaptive swimming behavior towards a target by optimizing combined network.

关键词： Artificial life evolutionary computations Learning Neural networks

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Genetic algorithms for spectral pattern recognition

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VIBRATIONAL SPECTROSCOPY 2002年第1期28卷 83-95页

作者： Lavine, BK Davidson, CE Moores, AJ Clarkson Univ Dept Chem Potsdam NY 13699 USA

The development of a genetic algorithm (GA) for pattern recognition analysis of spectral data is reported. The GA identifies a set of features (wavelengths) that optimize the separation of the classes in a plot of the two or three largest principal components of the data. Because principal components maximize variance, the bulk of the information encoded by the selected features is about differences between classes in the dataset. In addition, the GA focuses on those classes and or samples that are difficult to classify as it trains using a form of boosting to modify the fitness landscape. Boosting minimizes the problem of convergence to a local optimum since the fitness function of the GA is changing as the population is evolving towards a solution. Over time, samples that consistently classify correctly are not as heavily weighted in the analysis as samples that are difficult to classify. The pattern recognition GA learns its optimal parameters in a manner similar to a neural network. The algorithm integrates aspects of both strong and weak learning to yield a "smart" one-pass procedure. (C) 2002 Elsevier Science B.V. All rights reserved.

关键词： genetic algorithm evolutionary computations boosting spectral pattern recognition principal component analysis

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Genetic engineering of hierarchical fuzzy regional representations for handwritten character recognition

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INTERNATIONAL JOURNAL ON DOCUMENT ANALYSIS AND RECOGNITION 2006年第4期8卷 223-231页

作者： Gagne, Christian Parizeau, Marc Univ Laval Dept Genie Elect & Genie Infomat LVSN Quebec City PQ G1K 7P4 Canada

This paper presents a genetic programming based approach for optimizing the feature extraction step of a handwritten character recognizer. This recognizer uses a simple multilayer perceptron as a classifier and operates on a hierarchical feature space of orientation, curvature, and center of mass primitives. The nodes of the hierarchy represent rectangular sub-regions of their parent node, the tree root corresponding to the character's bounding box. Within each sub-region, a variable number of fuzzy features are extracted. Genetic programming is used to simultaneously learn the best hierarchy and the best combination of fuzzy features. Moreover, the fuzzy features are not predetermined, they are inferred from the evolution process which runs a two-objective selection operator. The first objective maximizes the recognition rate, and the second minimizes the feature space size. Results on Unipen data show that, using this approach, robust representations could be obtained that out-performed comparable human designed hierarchical fuzzy regional representations.

关键词： on-line character recognition handwriting evolutionary computations fuzzy logic unipen dataset

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The evolutionary dynamics of audit

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2012年第2期216卷 469-476页

作者： Anastasopoulos, Nikolaos P. Anastasopoulos, Markos P. Hellen Open Univ Sch Social Sci Patras Greece Technol Educ Inst Kalamata Dept Finance & Auditing Sch Management & Econ Antikalamos 24100 Greece Natl Tech Univ Athens Sch Elect & Comp Engn Athens 15780 Greece

A critical issue in auditing is provisioning of reasonable assurance that the financial reports are free from material misstatements. The auditing detection problem can be viewed as a two-player game between the auditor and the auditee where the auditor aims at eliminating misstatements, reducing at the same time his audit efforts, while the auditee aims at benefiting from fraudulent financial reporting and defalcation. In this paper, the auditing/fraud detection problem is modeled employing evolutionary game theory. It is proved that, given that the players have accurate information for the parameters involved in the problem, the auditing/fraud detection game is stable but not asymptotically stable. The case of the auditor being partially informed about the auditee firm is also studied and it is concluded that if the auditor is partially informed about the auditee firm, a more comprehensive audit is necessary to guarantee quality of audit. Finally, analytical results are derived concerning the impact of audit tenure on audit quality. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Auditing Decision analysis Dynamical systems Economics evolutionary computations Game theory

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On the resource renting problem with overtime

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COMPUTERS & INDUSTRIAL ENGINEERING 2017年 111卷 303-319页

作者： Kerkhove, Louis-Philippe Vanhoucke, Mario Maenhout, Broos Univ Ghent Fac Econ & Business Adm Tweekerkenstr 2 B-9000 Ghent Belgium Vlerick Business Sch Reep 1 B-9000 Ghent Belgium UCL UCL Sch Management Gower St London WC1E 6BT England

In this paper the Resource Renting Problem with Overtime (RRP/overtime) is presented. The RRP/overtime is a new problem in which the assumptions of the basic RRP are combined with the possibility to schedule (parts of) activities during overtime. The addition of this extension increases the applicability of the RRP to real world problems. This paper also presents a solution technique for this extension of the resource renting problem. The solution procedure uses a scatter search heuristic to optimize a priority list, which is then in turn used by a schedule generation scheme (PatSGS). A variation on this schedule generation scheme is also used in dedicated local search procedures. The third contribution of this research is a new lower bound for the RRP/overtime problem, which is used to evaluate the results of the proposed heuristic solution method. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Project scheduling Combinatorial optimization Decision support systems evolutionary computations Heuristics

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The problem of highly constrained tasks in group decision support systems

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2001年第1期135卷 220-229页

作者： Rees, J Barkhi, R Purdue Univ Krannert Grad Sch Management W Lafayette IN 47907 USA Virginia Polytech Inst & State Univ Pamplin Coll Business Dept Accounting & Informat Syst Blacksburg VA 24061 USA

Most experimental uses of group decision support systems (GDSS) are associated with relatively unrestricted domains, for example, idea generation and preference specification, where few restrictions on potential solutions exist. However, an important GDSS task is that of resource allocation across functional areas of the organization, including supply chain applications. These types of tasks, such as budget planning and production planning, are typically highly constrained and difficult to solve optimally, necessitating the use of decision aids, such as those found in GDSS. We use a model based on adaptive search of a genetic algorithm as the analogy for the group decision making process. We apply this model to experimental data gathered from GDSS groups solving a production planning task. The results indicate very low estimated crossover rates in the experimental data. We also run computational experiments based on adaptive search to mimic the GDSS data and find that the low estimated crossover rate might be due to the highly constrained search space explored by the decision making groups. The results suggest further investigation into the presumed beneficial effects of group interaction in such highly constrained task domains, as it appears very little true information exchange occurs between group members in such an environment. Furthermore, the simulation technique can be used to help predict certain GDSS behaviors, thus improving the entire GDSS process. (C) 2001 Elsevier Science B.V. All rights reserved.

关键词： group decisions and negotiations genetic algorithms evolutionary computations

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Adaptive memory programming:: A unified view of metaheuristics

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2001年第1期135卷 1-16页

作者： Taillard, ÉD Gambardella, LM Gendreau, M Potvin, JY Univ Appl Sci Western Switzerland EIVD CH-1400 Yverdon Switzerland SUPSI IDSIA Lugano Switzerland Univ Montreal Ctr Rech Transports Montreal PQ Canada Univ Montreal Dept Informat & Rech Operat Montreal PQ H3C 3J7 Canada

The paper analyses recent developments of a number of memory-based metaheuristics such as taboo search (TS), scatter search (SS), genetic algorithms (GA) and ant colonies. It shows that the implementations of these general solving methods are increasingly similar. So, a unified presentation is proposed under the name of adaptive memory programming (AMP). A number of methods recently developed for the quadratic assignment, vehicle routing and graph colouring problems are reviewed and presented under the AMP point of view. AMP presents a number of interesting aspects such as a high parallelization potential and the ability of dealing with real and dynamic applications. (C) 2001 Elsevier Science B.V. All rights reserved.

关键词： metaheuristics evolutionary computations genetic algorithms multi-agent systems taboo search quadratic assignment vehicle routing

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Automatic parameter learning method for agent activation spreading network by evolutionary computation

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ARTIFICIAL LIFE AND ROBOTICS 2023年第3期28卷 571-582页

作者： Shimokawa, Daiki Yoshida, Naoto Koyama, Shuzo Kurihara, Satoshi Keio Univ Grad Sch Sci & Technol Yokohama Kanagawa Japan Keio Univ Fac Sci & Technol Yokohama Kanagawa Japan

A variety of planning research is being actively conducted in multiple research fields. The focus of these studies is to flexibly utilize both immediate and deliberative planning in response to the environment and to adaptively prioritize multiple goals and actions in a human-like manner. To achieve this, a method that applies active propagation to multi-agent planning (agent activation spreading network) has been proposed and is being utilized in various research fields. Furthermore, with the recent development of large-scale artificial intelligence models, we should soon be able to incorporate tacit human knowledge into this architecture. However, there is not yet a method for adjusting the parameters in this architecture which creates a barrier to future extension. In response, we have developed a method for automatically adjusting the parameters using evolutionary computation. Our experimental results showed that (1) the proposed method enables a higher degree of adaptation, thanks to taking the agent's semantics into account, and (2) it is possible to obtain parameters that are appropriate to the environment even when the experimental environment is changed.

关键词： Artificial intelligence Multi-agent systems evolutionary computations Swarm intelligence

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Directed clonal selection algorithm for associative classification

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INTERNATIONAL JOURNAL OF MODELLING IDENTIFICATION AND CONTROL 2013年第3期18卷 218-225页

作者： Zhao, Lihua Pan, Jin Xidian Univ Minist Educ Key Lab Intelligent Percept & Image Understanding Xian 710106 Shaanxi Peoples R China Xian Commun Inst Xian 710106 Shaanxi Peoples R China

In this paper, we present an directed clonal selection algorithm (DCLONALG) for mining association rules. Different from the traditional evolutionary algorithms, DCLONALG firstly scans dataset one time to find frequent rules with only one item. Then, these items were used to generate new rules and the mutation operation was limited in it. We evaluate the performance of the proposed DCLONALG approach for AC and the performance results have shown that the proposed approach is efficient in dealing with the problem on the complexity of the rule search space. At the same time, good classification accuracy has been achieved. This is especially important for mining association rules from large datasets in which the search space of rules is huge.

关键词： clonal selection algorithm CLONALG artificial immune system AIS associative classification AC evolutionary computations

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Tackling a VRP challenge to redistribute scarce equipment within time windows using metaheuristic algorithms

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EURO JOURNAL ON TRANSPORTATION AND LOGISTICS 2019年第5期8卷 561-595页

作者： Kheiri, Ahmed Dragomir, Alina G. Mueller, David Gromicho, Joaquim Jagtenberg, Caroline van Hoorn, Jelke J. Univ Lancaster Management Sch Dept Management Sci Lancaster LA1 4YX England Univ Vienna Fac Business Econ & Stat Vienna Austria Vienna Univ Technol Inst Theoret Phys Vienna Austria ORTEC Houtsingel 5 NL-2719 EA Zoetermeer Netherlands ORTEC Houtsingel 5 NL-2719 EA Zoetermeer Netherlands Vrije Univ Sch Business & Econ De Boelelaan 1105 Amsterdam Netherlands

This paper reports on the results of the VeRoLog Solver Challenge 2016-2017: the third solver challenge facilitated by VeRoLog, the EURO Working Group on Vehicle Routing and Logistics Optimization. The authors are the winners of second and third places, combined with members of the challenge organizing committee. The problem central to the challenge was a rich VRP: expensive and, therefore, scarce equipment was to be redistributed over customer locations within time windows. The difficulty was in creating combinations of pickups and deliveries that reduce the amount of equipment needed to execute the schedule, as well as the lengths of the routes and the number of vehicles used. This paper gives a description of the solution methods of the above-mentioned participants. The second place method involves sequences of 22 low level heuristics: each of these heuristics is associated with a transition probability to move to another low level heuristic. A randomly drawn sequence of these heuristics is applied to an initial solution, after which the probabilities are updated depending on whether or not this sequence improved the objective value, hence increasing the chance of selecting the sequences that generate improved solutions. The third place method decomposes the problem into two independent parts: first, it schedules the delivery days for all requests using a genetic algorithm. Each schedule in the genetic algorithm is evaluated by estimating its cost using a deterministic routing algorithm that constructs feasible routes for each day. After spending 80 percent of time in this phase, the last 20 percent of the computation time is spent on Variable Neighborhood Descent to further improve the routes found by the deterministic routing algorithm. This article finishes with an in-depth comparison of the results of the two approaches.

关键词： Routing evolutionary computations Metaheuristics Inventory

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