IEEE Transactions on Evolutionary Computation

The paper proposes an algorithm for data mining called Ant-Miner (ant-colony-based data miner). The goal of Ant-Miner is to extract classification rules from data. The algorithm is inspired by both research on the behavior of real ant colonies and some data mining concepts as well as principles. We compare the performance of Ant-Miner with CN2, a well-known data mining algorithm for classification, in six public domain data sets. The results provide evidence that: 1) Ant-Miner is competitive with CN2 with respect to predictive accuracy, and 2) the rule lists discovered by Ant-Miner are considerably simpler (smaller) than those discovered by CN2.

We prove some convergence properties for a class of ant colony optimization algorithms. In particular, we prove that for any small constant /spl epsiv/ > 0 and for a sufficiently large number of algorithm iterations t, the probability of finding an optimal solution at least once is P*(t) /spl ges/ 1 - /spl epsiv/ and that this probability tends to 1 for t/spl rarr//spl infin/. We also prove that, after an optimal solution has been found, it takes a finite number of iterations for the pheromone trails associated to the found optimal solution to grow higher than any other pheromone trail and that, for t/spl rarr//spl infin/, any fixed ant will produce the optimal solution during the tth iteration with probability P /spl ges/ 1 /spl epsiv//spl circ/(/spl tau//sub min/, /spl tau//sub max/), where /spl tau//sub min/ and /spl tau//sub max/ are the minimum and maximum values that can be taken by pheromone trails.

Chiến lược thời gian thực (RTS) là một tiểu thể loại của trò chơi video chiến lược, thường liên quan đến việc thu thập tài nguyên, xây dựng căn cứ, lập kế hoạch chiến lược và các kịch bản chiến đấu. Với lối chơi phức tạp, không gian trạng thái và hành động rộng lớn, các trò chơi RTS đã được chứng minh là một nền tảng xuất sắc cho nghiên cứu trí tuệ nhân tạo. Một trong những vấn đề thách thức lớn nhất mà các trò chơi RTS đặt ra đó là kiểm soát chi tiết các đơn vị trong chiến đấu, tức là, quản lý đơn vị một cách vi mô. Trong bài báo này, chúng tôi trình bày một phương pháp tích hợp tích phân mờ và tìm kiếm thuật toán heuristic nhanh để cải thiện chất lượng quản lý đơn vị trong trò chơi RTS nổi tiếng StarCraft. Các thí nghiệm sẽ được báo cáo ở phần cuối của bài báo, cho thấy kết quả hứa hẹn và tiềm năng của phương pháp được đề xuất trong lĩnh vực này.

Hypoglycemia (or low blood glucose) is dangerous for Type 1 diabetes mellitus (T1DM) patients, as this can cause unconsciousness or even death. However, it is impossible to monitor the hypoglycemia by measuring patients' blood glucose levels all the time, especially at night. In this paper, a hypoglycemic episode diagnosis system is proposed to determine T1DM patients' blood glucose levels based on these patients' physiological parameters which can be measured online. It can be used not only to diagnose hypoglycemic episodes in T1DM patients, but also to generate a set of rules, which describe the domains of physiological parameters that lead to hypoglycemic episodes. The hypoglycemic episode diagnosis system addresses the limitations of the traditional neural network approaches which cannot generate implicit information. The performance of the proposed hypoglycemic episode diagnosis system is evaluated by using real T1DM patients' data sets collected from the Department of Health, Government of Western Australia, Australia. Results show that satisfactory diagnosis accuracy can be obtained. Also, explicit knowledge can be produced such that the deficiency of traditional neural networks can be overcome. A clear understanding of how they perform diagnosis can be indicated.

We present a genetic algorithm (GA) that uses a slicing tree construction process for the placement and area optimization of soft modules in very large scale integration floorplan design. We have overcome the serious representational problems usually associated with encoding slicing floorplans into GAs and have obtained excellent (often optimal) results for module sets with up to 100 rectangles. The slicing tree construction process used by our GA to generate the floorplans has a runtime scaling of O(n lg n). This compares very favorably with other recent approaches based on nonslicing floorplans that require much longer runtimes. We demonstrate that our GA outperforms a simulated annealing implementation with the same representation and mutation operators as the GA.

Hầu hết các ứng dụng của các thuật toán tiến hóa đều liên quan đến các vấn đề tối ưu hóa tĩnh. Tuy nhiên, trong những năm gần đây, đã có sự quan tâm ngày càng tăng đối với các vấn đề thay đổi theo thời gian (động), thường thấy trong các kịch bản thực tế. Một thách thức lớn trong lĩnh vực này là thiết kế các bộ phát sinh trường hợp thử nghiệm (TCGs) thực tế, điều này đòi hỏi phải phân tích hệ thống các nhiệm vụ tối ưu hóa động. Đến nay, chỉ có một vài TCG được đề xuất. Cuộc điều tra của chúng tôi dẫn đến kết luận rằng các TCG này không có khả năng phát sinh các bài kiểm tra chuẩn động thực tế. Kết quả nghiên cứu của chúng tôi là thiết kế một TCG mới có khả năng tạo ra các cảnh quan không ổn định thực tế.

A local navigation algorithm for mobile robots is proposed that combines rule-based and neural network approaches. First, the extended virtual force field (EVFF), an extension of the conventional virtual force field (VFF), implements a rule base under the potential field concept. Second, the neural network performs fusion of the three primitive behaviors generated by EVFF. Finally, evolutionary programming is used to optimize the weights of the neural network with an arbitrary form of objective function. Furthermore, a multinetwork version of the fusion neural network has been proposed that lends itself to not only an efficient architecture but also a greatly enhanced generalization capability. Herein, the global path environment has been classified into a number of basic local path environments to which each module has been optimized with higher resolution and better generalization. These techniques have been verified through computer simulation under a collection of complex and varying environments.

Negotiations are a special class of group decision-making problems that can be formulated as constrained optimization problems and are characterized by high degrees of conflict among the negotiation participants. A variety of negotiation support techniques have been used to help find solutions acceptable to all parties in a negotiation. The paper presents an approach that employs a genetic algorithm (GA) for finding acceptable solutions for multiparty multiobjective negotiations. The GA approach is consistent with the complex nature of real-world negotiations and is therefore capable of addressing more realistic negotiation scenarios than previous techniques in the literature allow. In addition to the traditional genetic operators of reproduction, crossover, and mutation, the search is enhanced with a new operator called trade. The trade operator simulates concessions that might be made by parties during the negotiation process. GA performance with the trade operator is compared to a traditional GA, nonlinear programming, a hill-climber, and a random search. Experimental results show the GA with the trade operator performs better than these other more traditional approaches.