Titlebook: Oppositional Concepts in Computational Intelligence; Hamid R. Tizhoosh,Mario Ventresca Book 2008 Springer-Verlag Berlin Heidelberg 2008 Op

Archipelago

Improving the Exploration Ability of Ant-Based Algorithmson technique that has been used to solve many complex problems. Despite its successes, ACO is not a perfect algorithm: it can remain trapped in local optima, miss a portion of the solution space or, in some cases, it can be slow to converge. Thus, we were motivated to improve the accuracy and conver

Inertia

持續(xù)

Evolving Opposition-Based Pareto Solutions: Multiobjective Optimization Using Competitive Coevolutiot learning, neural networks, swarm intelligence and simulated annealing. However, an area of research that is still in infancy is the application of the OBL concept to coevolution. Hence, in this chapter, two new opposition-based competitive coevolution algorithms for multiobjective optimization cal

HALO

Bayesian Ying-Yang Harmony Learning for Local Factor Analysis: A Comparative Investigationpriately, which is a typical example of model selection. One conventional approach for model selection is to implement a two-phase procedure with the help of model selection criteria, such as AIC, CAIC, BIC(MDL), SRM, CV, etc.. Although all working well given large enough samples, they still suffer

Bureaucracy

Microgram

Two Frameworks for Improving Gradient-Based Learning Algorithmsowards very long training times and convergence to local optima. Various methods have been proposed to alleviate these issues including, but not limited to, different training algorithms, automatic architecture design and different transfer functions. In this chapter we continue the exploration into

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Opposite Actions in Reinforced Image Segmentationf sufficient number of training samples is usually an obstacle, especially when the samples need to be manually prepared by an expert. In addition, none of the existing methods uses online feedback from the user in order to evaluate the generated results and continuously improve them. Considering th

倫理學(xué)

Opposition Mining in Reservoir Managementoptimization or simulation techniques have been developed and applied to capture the complexities of the problem; however, most of them suffered from the curse of dimensionality. Q-learning as a popular and simulation-based method in Reinforcement Learning (RL) might be an efficient way to cope well

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