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Author Archives: Yarpiz

Feature Selection using Metaheuristics and EAs

Feature selection is one of common preprocessing tasks, which is performed to reduce the number of inputs of intelligent algorithms and models. This helps us to simplify the models, reduce the computation cost of model training, and enhance the generalization abilities of the model and prevention of over-training. For more information on feature selection concepts and methods, you can refer ...

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Nonlinear Regression using ANFIS

Adaptive Neuro-Fuzzy Inference System (ANFIS) is a combination of artificial neural network (ANN) and Takagi-Sugeno-type fuzzy system, and it is proposed by Jang, in 1993, in this paper. ANFIS inherits the benefits of both neural networks and fuzzy systems; so it is a powerful tool, for doing various supervised learning tasks, such as regression and classification. Fuzzy Logic Toolbox provides ...

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Bees Algorithm (BeA) in MATLAB

Bees Algorithm (BeA) is a metaheuristic optimization algorithm, inspired by food foraging behavior of honey bee colonies, and proposed by Pham et al., in 2005. In this algorithm, the mechanism of Waggle Dance is used to simulate the communication between bees. Better bees (solutions) have more opportunity to do waggle dance, and hence they are capable of attract more bees ...

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Group Method of Data Handling (GMDH) in MATLAB

Group Method of Data Handling (GMDH) is a family of mathematical modeling and nonlinear regression algorithms, which is originally proposed by Alexey Grigorevich Ivakhnenko, an Ukrainian scientist and mathematician, in 1968. This approach is also known as Polynomial Neural Network and can be assumed as a specific type of supervised Artificial Neural Network (ANN). In addition to modeling specifications, GMDH ...

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Firefly Algorithm (FA) in MATLAB

Firefly Algorithm (FA) is a metaheuristic algorithm for global optimization, which is inspired by flashing behavior of firefly insects. This algorithm is proposed by Xin-She Yang in 2008. Fireflies use the flashing behavior to attract other fireflies, usually for sending signals to opposite sex. However, in the mathematical model, used inside Firefly Algorithm, simply the fireflies are unisex, and any ...

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DBSCAN Clustering in MATLAB

Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is a density-based clustering algorithm, proposed by Martin Ester et al., 1996. The algorithm finds neighbors of data points, within a circle of radius ε, and adds them into same cluster. For any neighbor point, which its ε-neighborhood contains a predefined number of points, the cluster is expanded to contain its neighbors, ...

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Invasive Weed Optimization (IWO) in MATLAB

Invasive Weed Optimization (IWO) is a nature-inspired metaheuristic, inspired by spreading strategy of weeds, and proposed by Alireza Mehrabian and Caro Lucas, in 2006. Based on the r/K Selection Theory, the artificial weeds (solutions) use the r-Selection strategy in the beginning of algorithms, and gradually they switch to K-Selection strategy, as algorithm continues to running. For more information on the ...

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Imperialist Competitive Algorithm (ICA) in MATLAB

Imperialist Competitive Algorithm (ICA), also known as Colonial Competitive Algorithm (CCA), is a sociopolitical metaheuristics, inspired by historical colonization process and competition among imperialists, to capture more colonies. Set of countries (solutions) in ICA, is partitioned to form several Empires, which contains a single Imperialist and several other weaker countries, called Colonies. This algorithm is proposed by Esmaeil Atashpaz Gargari ...

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Tabu Search (TS) in MATLAB

Tabu Search (TS) is a local search-based metaheuristic, which is proposed by Fred W. Glover, in 1986. Tabu Search is completely based on the definition of neighborhood and actions converting a solution to its neighboring solutions. This algorithms starts with a single solution, and searches for better solutions, applying actions and moving between neighbor solutions. However, acceptance, applicability and availability ...

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Biogeography-Based Optimization (BBO) in MATLAB

Biogeography-Based Optimization (BBO) is an evolutionary algorithm and metaheuristic, which is inspired by the biogeographic concepts: speciation (the evolution of new species), the migration of species between islands, and the extinction of species. The algorithm is originally proposed by Dan Simon, in 2008, in this paper. This algorithm is based on a mathematical model, describing the migration of species between ...

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