Yarpiz Academic Source Codes and Tutorials

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 ...

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 ...

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 ...

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 ...

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. ...

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 ...

Non-dominated Sorting Genetic Algorithm II (NSGA-II) is a multi-objective genetic algorithm, proposed by Deb et al., in 2002. It is an extension and improvement of NSGA, which is proposed earlier by Srinivas and Deb, in 1995. In the structure of NSGA-II, in addition to genetic operators, crossover and mutation, two specialized multi-objective operators and mechanisms are defined and utilized: Non-dominated ...

Ant Colony Optimization (ACO) are a set of probabilistic metaheuristics and an intelligent optimization algorithms, inspired by social behavior of ants. ACO algorithms are also categorized as Swarm Intelligence methods, because of implementation of this paradigm, via simulation of ants behavior in the structure of these algorithms. First ACO algorithm is proposed by Marco Dorigo in his PhD thesis, in ...

Particle Swarm Optimization (PSO) is an intelligent optimization algorithm based on the Swarm Intelligence. It is based on a simple mathematical model, developed by Kennedy and Eberhart in 1995, to describe the social behavior of birds and fish. The model relies mostly on the basic principles of self-organization which is used to describe the dynamics of complex systems. Swarm intelligence ...

Genetic Algorithms (GAs) are most famous Evolutionary Algorithms (EAs) which are inspired from natural evolution and selection. Their main application is in the field of optimization. Hence they are applicable to any kind of problem, which can be converted or stated as an optimization task. To read more about Genetic Algorithms, you can see the related article in Wikipedia (here). ...