Meta-heuristics optimization methods are important techniques for optimal design of the engineering systems. Numerous methods, inspired by different nature phenomena, have been introduced in the literature. A new modified version of Teaching-Learning-Based Optimization (TLBO) Algorithm is introduced in this paper. TLBO, as a parameter free algorithm, is based on the learning procedure of studen...

neural network is one of the most widely used algorithms in the field of machine learning, on the other hand, neural network training is a complicated and important process. supervised learning needs to be organized to reach the goal as soon as possible. a supervised learning algorithm analyzes the training data and produces an inferred function, which can be used for mapping new examples.  hen...

The present study investigated attentional optimization in participants learning rule-based (RB) and information integration (II) categories. Using an eye-tracker to measure the deployment of overt attention, we tracked participants’ learning and optimization during a category learning experiment. We also measured working memory span. We found that participants in the RB condition optimized att...

This paper justifies and demonstrates a machine learning approach to the problem of texture recognition. The learning-based texture recognition is separated into the following phases: (i) the acquisition of texture concepts, (ii) the optimization of concept prototypes, and (iii) the recognition of unknown texture samples. Methodology adapted to the acquisition and recognition of-noisy texture d...

Particle swarm optimization (PSO) is a heuristic optimization technique based on swarm intelligence that is inspired by the behavior of bird flocking. The canonical PSO has the disadvantage of premature convergence. Several improved PSO versions do well in keeping the diversity of the particles during the searching process, but at the expense of rapid convergence. This paper proposes an example...

This talk will cover the main components of sequential modelbased optimization algorithms. Algorithms of this kind represent the state-of-the-art for expensive black-box optimization problems and are getting increasingly popular for hyper-parameter optimization of machine learning algorithms, especially on larger data sets. The talk will cover the main components of sequential model-based optim...