Real-time strategy (RTS) games have become one of the hotspots in the field of artificial intelligence research due to the large search space, long-term planning, and real-time constraint. In view of the fact that mos...
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We describe a new, quadruped robot platform, Aracna, which requires non-intuitive motor commands in order to locomote and thus provides an interesting challenge for gait learning algorithms, such as those frequently d...
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The Traveling Salesman Problem is a typical NP-Hard combinatorial optimization problem. This paper proposes an evolutionary algorithm based on multi-players game theory (EAMG) for the TSP. EAMG transforms TSP to an n-...
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Portfolio optimization is a well-known problem in the domain of finance with reports dating as far back as 1952. It aims to find a trade-off between risk and expected return for the investors, who want to invest finit...
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作者：
Fang, WeiZhang, QiangSun, JunWu, XiaojunJiangnan University
International Joint Laboratory on Artificial Intelligence of Jiangsu Province Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Wuxi China
Most studies on pattern mining have considered only one pattern, such as frequent pattern or high-utility pattern, which is difficult to meet the increasingly diverse needs of users. In this paper, a novel multi-objec...
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Sefrioui introduced the Nash Genetic Algorithm in *** approach combines genetic algorithms with Nash's idea. Another central achievement of Game Theory is the introduction of an evolutionary Stable Strategy, intro...
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An unsupervised fuzzy clustering technique, fuzzy c-means (FCM) clustering algorithm has been widely used in image segmentation. However, the FCM algorithm always converges to strict local minima, starting from an ini...
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We present a new classification system based on evolutionary Algorithm (EA), OBLIC. This tool is an OBLIque Classification system whose function is to induce a set of classification rules no hierarchical from a databa...
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This paper proposes an evolutionary algorithm with lowerdimensional- search crossover for constrained engineering optimization problems. Crossover operator of the algorithm searches a lower dimensional space determine...
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A New Linear Optimization technique coupling evolutionary algorithm for Solving Multiobjective Optimization Problems (NLEA) based on real-coded method is proposed after analyzing the drawbacks of existing evolutionary...
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ISBN:
(纸本)9780769537368
A New Linear Optimization technique coupling evolutionary algorithm for Solving Multiobjective Optimization Problems (NLEA) based on real-coded method is proposed after analyzing the drawbacks of existing evolutionary algorithms in this paper. One of the main advantages of the proposed approach is that search space of constrained dominance problems with high dimensions is compressed into two dimensions. NLEA has a linear fitness function in two dimension space so as to evaluate fitness of each individual fast in population. A crossover operator based on density function and a new mutation operator is developed to extend the search space and extract the better solution. In our tests, A few benchmark multi-objective optimization problems which divided into two groups are taken to test this algorithm. The numerical experiments show that proposed approach is feasible and effective, and provides good performance in terms of uniformity and diversity of solutions. Multiobjective optimization problems, evolutionary algorithms, Pareto optimal solutions, Linear function.
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