Optimization, learning and natural algorithms 来自 ResearchGate 喜欢 0 阅读量: 1475 作者: M Dorigo 摘要: Publication » Learning and Natural Algorithms. DOI: http://dx.doi.org/ 被引量: 4624 年份: 1992 收藏 引用 批量引用 报错 分享 全部来源 求助全文 ResearchGate ...
(1992). Optimization, Learning and Natural Algorithms, Politecnico di Milano, Italy, Ph.D. Thesis. Google Scholar Draa, 2015 A. Draa On the performances of the flower pollination algorithm – Qualitative and quantitative analyses Applied Soft Computing, 34 (2015), pp. 349-371, 10.1016/j.asoc...
The first two classes are mainly used for parameter optimization (with the exception of genetic programming, a specific type of evolutionary algorithms) whereas the third class is applied for learning the structures of controllers. As such, the methods selected illustrate the main concepts of natural...
They utilized the Logistic model and refraction learning strategy in the improved algorithm and applied it to solve high-dimensional optimization problems, two engineering design problems, and the photovoltaic model parameter estimation problem. Comparative analysis against other algorithms demonstrated its ...
Another category of optimization algorithms is based on the origin of inspiration from biological evolution, genetics, and natural selection. The genetic optimization algorithm (GA)69is one of the most well-known algorithms in this category. Among the notable algorithms in this category are Memetic ...
Sixthly, Opposition-based learning (OBL)—OBL is a fast-growing research area that has sparked widespread interest in the last few decades. The idea of OBL has earlier been incorporated by numerous soft computing algorithms, including optimization methods, fuzzy systems, reinforcement learning, and ...
David Goldberg's Genetic Algorithms in Search, Optimization and Machine Learning is by far the bestselling introduction to genetic algorithms. Goldberg is one of the preeminent researchers in the field--he has published over 100 research articles on genetic algorithms and is a student of ...
Course 2: Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization Week 1 Quiz - Practical aspects of deep learning:Text|PDF Week 2 Quiz - Optimization algorithms:Text|PDF Week 3 Quiz - Hyperparameter tuning, Batch Normalization, Programming Frameworks:Text|PDF ...
The main goal of machine learning is the creation of self-learning algorithms in many areas of human activity. It allows a replacement of a person with artificial intelligence in seeking to expand production. The theory of artificial neural networks, whi
To overcome the disadvantages of premature convergence and easy trapping into local optimum solutions, this paper proposes an improved particle swarm optimization algorithm (named NDWPSO algorithm) based on multiple hybrid strategies. Firstly, the elite opposition-based learning method is utilized to init...