Titlebook: Simulated Evolution and Learning; Second Asia-Pacific Bob McKay,Xin Yao,Takeshi Furuhashi Conference proceedings 1999 Springer-Verlag Berl

悬崖

lanugo

Evolution of Reference Sets in Nearest Neighbor Classification,is designed by selecting a small number of reference patterns from a large number of training patterns using a genetic algorithm. The genetic algorithm also removes unnecessary features. The reference set in our nearest neighbor classification consists of selected patterns with selected features. A

行乞

反复拉紧

Quantifying Neighborhood Preservation: Joint Properties of Evolutionary and Unsupervised Neural Lea contrast to supervised learning algorithms unsupervised neural networks have their objective function implicitly defined by the learning rule. When considering topographic mapping as an optimization problem, the presence of explicitly defined objective functions becomes essential. In this paper, we

汇总

Neural Networks and Evolutionary Algorithms for the Prediction of Thermodynamic Properties for Chem them. We compare backpropagation trained networks and evolution strategy trained networks with two physical models. Experimental data for the enthalpy of vaporization were taken from the literature in our investigation. The input information for both neural network and physical models consists of p

Guaff豪情痛饮

合唱队

Libido

Bureaucracy

Evolving Logic Programs to Classify Chess-Endgame Positions,programming (ILP). Given input of positive and negative examples, the algorithm constructs a logic program to classify these examples. The algorithm has several attractive features including the ability to explicitly use background (user-supplied) knowledge and to produce comprehensible output. We p

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Natural Computation,well as Natural Computation. An outline of Evolutionary Algorithms (EA - the common denominator for GA, EP, and ES) will be sketched, their differences pinpointed, some theoretical results summarized, and some applications mentioned.