cost optimization of the reinforced concrete cantilever soil retaining wall of a given height satisfying some structural and geotechnical design constraints is performed utilizing harmony search and improved harmony search algorithms. the objective function considered is the cost of the structure, and design is based on aci 318-05. this function is minimized subjected to design constraints. a n...

In the GA approach the parameters that influence its performance include population size, crossover rate and mutation rate. Genetic algorithms are suitable for traversing large search spaces since they can do this relatively fast and because the mutation operator diverts the method away from local optima, which will tend to become more common as the search space increases in size. GA’s are base...

Redundant and irrelevant features in high dimensional data increase the complexity in underlying mathematical models. It is necessary to conduct pre-processing steps that search for the most relevant features in order to reduce the dimensionality of the data. This study made use of a meta-heuristic search approach which uses lightweight random simulations to balance between the exploitation of ...

The Hilbert-Huang transform (HHT) is a novel, adaptive approach to time series analysis. It does not impose a basis set on the data or otherwise make assumptions about the data form, and so the time–frequency decomposition is not limited by spreading due to uncertainty. Because of the high resolution of the time–frequency, we investigate the possibility of the application of the HHT to the sear...

We present a heuristic meta-learning search method for finding a set of optimized algorithmic parameters for a range of machine learning algorithms. The method, wrapped progressive sampling, is a combination of classifier wrapping and progressive sampling of training data. A series of experiments on UCI benchmark data sets with nominal features, and five machine learning algorithms to which sim...

This paper presents an optimisation technique to automatically select a set of control parameters for a Markov Random Field. The method is based on the Reactive Tabu Search strategy, and requires to define a suitable fitness function that measures the performance of the MRF algorithm with a given parameters set. The technique is applied to stereo matching thanks to the availability of ground-tr...

In case of optimization of biochemical networks the best combination of adjustable parameters has to be found keeping low the costs of modification of biochemical network and reducing the risk of unpredicted side effects of processes outside the scope of the model. Dynamic models of biochemical networks usually are in form of system of differential equations and therefore can not be optimized a...

The performance of several network protocols can be significantly enhanced by tuning their parameters. The optimization of network protocol parameters can be modeled as a “black-box” optimization problem with unknown, multi-modal and noisy objective functions. In this paper, a recursive random search algorithm is proposed to address this type of optimization problems. The new algorithm takes ad...

The rapid development of the internet eventually increases the number of internet users triggering the need for an intelligent search engine that is able to minimize the search on world wide web (WWW) and find relevant information as requested. To overcome the issue of finding relevant information as well as minimizing the search on WWW, this paper proposes a search engine that is specifically ...