Search Results - (( evolution classification parallel algorithm ) OR ( using normalization bat algorithm ))

  • Showing 1 - 7 results of 7
Refine Results
  1. 1

    Taguchi?s T-method with Normalization-Based Binary Bat Algorithm by Marlan Z.M., Jamaludin K.R., Harudin N.

    Published 2025
    “…Specifically, a normalization-based Binary Bat algorithm is used, where discretization of continuous solution into binary form is performed using a normalization equation. …”
    Conference paper
  2. 2

    In silico gene knockout prediction using a hybrid of Bat algorithm and minimization of metabolic adjustment by Man, Mei Yen, Mohd Saberi, Mohamad, Choon, Yee Wen, Mohd Arfian, Ismail

    Published 2021
    “…Metabolic and genetic engineering is important in producing the chemicals of interest as, without them, the product yields of many microorganisms are normally low. As a result, the aim of this paper is to propose a combination of the Bat algorithm and the minimization of metabolic adjustment (BATMOMA) to predict which genes to knock out in order to increase the succinate and lactate production rates in Escherichia coli (E. coli).…”
    Get full text
    Get full text
    Get full text
    Article
  3. 3

    Artificial neural network learning enhancement using Artificial Fish Swarm Algorithm by Hasan, Shafaatunnur, Tan, Swee Quo, Shamsuddin, Siti Mariyam, Sallehuddin, Roselina

    Published 2011
    “…Artificial Neural Network (ANN) is a new information processing system with large quantity of highly interconnected neurons or elements processing parallel to solve problems.Recently, evolutionary computation technique, Artificial Fish Swarm Algorithm (AFSA) is chosen to optimize global searching of ANN.In optimization process, each Artificial Fish (AF) represents a neural network with output of fitness value.The AFSA is used in this study to analyze its effectiveness in enhancing Multilayer Perceptron (MLP) learning compared to Particle Swarm Optimization (PSO) and Differential Evolution (DE) for classification problems.The comparative results indeed demonstrate that AFSA show its efficient, effective and stability in MLP learning.…”
    Get full text
    Get full text
    Get full text
    Conference or Workshop Item
  4. 4
  5. 5
  6. 6
  7. 7