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Author Archives: Yarpiz

Apriori Association Rule Mining in MATLAB

Association Rule Mining is a common task in the field of Data Mining, involving the recognition of frequent patterns, usually in transactional databases. For example, discovering a rule like {bread, butter} → {milk} in a sales dataset is a result of association rule mining, and indicates that if a customer buys bread and butter, it is likely that they will buy ...

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PESA-II in MATLAB

Pareto Envelope-based Selection Algorithm II (PESA-II) is a multi-objective evolutionary optimization algorithm, which uses the mechanism of genetic algorithm together with selection based on Pareto envelope. PESA-II uses an external archive to store the approximate Pareto solutions. Parents and mutants are selected from this external archive, based on the grids created based on the geographical distribution of archive members. This ...

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Teaching-Learning-based Optimization in MATLAB

Teaching-Learning-based Optimization (TLBO) is a metaheuristic, inspired by process of Teaching and Learning, via a simplified mathematical model of knowledge improvements gained by students in a class. This algorithm is proposed by Rao, Savsani and Vakharia in 2011, in this paper. In this post, we are going to share with you, the open-source MATLAB implementation of Teaching-Learning-based Optimization (TLBO) algorithm. ...

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Shuffled Complex Evolution in MATLAB

Shuffled Complex Evolution (SCE-UA) is a metaheuristic for global optimization, proposed by Duan, Gupta and Sorooshian, in 1992. Because SCE is the abbreviated name of other methods in the science, the UA is added to the abbreviated name of this algorithm, because the creators of this algorithm are members of University of Arizona. In SCE-UA, the population is divided into ...

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Neural Gas and GNG Networks in MATLAB

Neural Gas network is a competitive Artificial Neural Network (ANN), very similar to Self-Organizing Map (SOM), which is proposed by Martinetz and Schulten, 1991. Neural Gas network can be used to solve unsupervised learning tasks, like clustering, dimensionality reduction, and topology learning. It has many applications in the fields of pattern recognition, data compression, speech recognition, and image segmentation. For ...

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Strength Pareto Evolutionary Algorithm 2 in MATLAB

Strength Pareto Evolutionary Algorithm 2 (SPEA2) is an extended version of SPEA multi-objective evolutionary optimization algorithm. This algorithm utilized a mechanism like k-Nearest Neighbor (kNN) and a specialized ranking system to sort the members of the population, and select the next generation of population, from combination of current population and off-springs created by genetic operators (mutation and crossover). SPEA2 is ...

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Shuffled Frog Leaping Algorithm in MATLAB

Shuffled Frog Leaping Algorithm (SFLA) is a metaheuristic, or more accurately it is a Memetic Algorithm, which is inspired by frog leaping. SFLA is based on the model used by Shuffled Complex Evolution (SCE-UA), and incorporated the memetic evolution into it. It is applicable to any kind of optimization problems, discrete, continuous or mixed, via modification of operators used in ...

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ACO for Continuous Domains in MATLAB

Originally, the Ant Algorithms are used to solve discrete and combinatorial optimization problems. Various extensions of Ant Colony Optimization (ACO) are proposed to deal with optimization problems, defined in continuous domains. One of the most useful algorithms of this type, is ACOR, the Ant Colony Optimization for Continuous Domains, proposed by Socha and Dorigo, in 2008 (here). ACOR is an ...

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Evolutionary Data Clustering in MATLAB

Clustering is an unsupervised machine learning task and many real world problems can be stated as and converted to this kind of problems. Clustering is grouping a set of  data objects is such a way that similarity of members of a group (or cluster) is maximized and on the other hand, similarity of members in two different groups, is minimized. ...

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Multi-Objective PSO in MATLAB

Multi-Objective Particle Swarm Optimization (MOPSO) is proposed by Coello Coello et al., in 2004. It is a multi-objective version of PSO which incorporates the Pareto Envelope and grid making technique, similar to Pareto Envelope-based Selection Algorithm to handle the multi-objective optimization problems. Just like PSO, particle in MOPSO are sharing information and moving towards global best particles and their own ...

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