GECCO Paper Published
Congratulations to Parham Hadikhani who published a paper "Improved data clustering using multi-trial vector-based differential evolution with gaussian crossover"
PhD Researcher Parham Hadikhani’s paper on "Improved data clustering using multi-trial vector-based differential evolution with gaussian crossover" has been accepted as a poster at GECCO 2022.
According to Wikipedia,
The Genetic and Evolutionary Computation Conference (GECCO) is the premier conference in the area of genetic and evolutionary computation. GECCO has been held every year since 1999, when it was first established as a recombination of the International Conference on Genetic Algorithms (ICGA) and the Annual Genetic Programming Conference (GP).
GECCO presents the latest high-quality results in genetic and evolutionary computation. Topics of interest include: genetic algorithms, genetic programming, evolution strategies, evolutionary programming, estimation of distribution algorithms, memetic algorithms, hyper-heuristics, evolutionary robotics, evolvable hardware, artificial life, ant colony optimization algorithms, swarm intelligence, artificial immune systems, digital entertainment technologies, evolutionary art, evolutionary combinatorial optimization, metaheuristics, evolutionary multi-objective optimization, evolutionary machine learning, search-based software engineering, theory, real-world applications, and more.
In this paper, Parham improved the Multi-Trial Vector-based Differential Evolution for clustering data.
The abstract of his paper:
Many algorithms have been proposed to solve the clustering problem. However, most of them lack a proper strategy to maintain a good balance between exploration and exploitation to prevent premature convergence. Multi-Trial Vector-based Differential Evolution (MTDE) is an improved differential evolution (DE) algorithm that is done by combining three strategies and distributing the population between these strategies to avoid getting stuck at a local optimum. In addition, it records inferior solutions to share information about visited regions with solutions of the next generations. In this paper, an Improved version of the Multi-Trial Vector-based Differential Evolution (IMTDE) algorithm is proposed and adapted for clustering data. The purpose here is to enhance the balance between the exploration and exploitation mechanisms in MTDE by employing Gaussian crossover and modifying the sub-population distribution between the strategies. To evaluate the performance of the proposed clustering, 19 datasets with different dimensions, shapes, and sizes were employed. The obtained results of IMTDE demonstrate improvement in MTDE performance by an average of 12%. Our comparative study with state-of-the-art algorithms demonstrates the superiority of IMTDE in most of these datasets because of the effective search strategies and the sharing of previous experiences in generating more promising solutions. Source code is available on Github: https://github.com/parhamhadikhani/IMTDE-Clustering.
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