scholarly journals Machine Coded Compact Genetic Algorithms for Real Parameter Optimization Problems

2020 ◽  
pp. 43-58
Author(s):  
Mehmet Hakan SATMAN ◽  
Emre AKADAL
Keyword(s):  
Genetic Algorithms ◽  
Parameter Optimization ◽  
Optimization Problems ◽  
Real Parameter

A Computationally Efficient Evolutionary Algorithm for Real-Parameter Optimization

2002 ◽  
Vol 10 (4) ◽  
pp. 371-395 ◽  
Author(s):  
Kalyanmoy Deb ◽  
Ashish Anand ◽  
Dhiraj Joshi
Keyword(s):  
Parameter Optimization ◽  
Optimization Problems ◽  
Research Effort ◽  
Probability Distributions ◽  
Scale Up ◽  
Extensive Study ◽  
Evolution Strategy ◽  
Real Parameter ◽  
Test Problems ◽  
Main Research

Due to increasing interest in solving real-world optimization problems using evolutionary algorithms (EAs), researchers have recently developed a number of real-parameter genetic algorithms (GAs). In these studies, the main research effort is spent on developing an efficient recombination operator. Such recombination operators use probability distributions around the parent solutions to create an offspring. Some operators emphasize solutions at the center of mass of parents and some around the parents. In this paper, we propose a generic parent-centric recombination operator (PCX) and a steady-state, elite-preserving, scalable, and computationally fast population-alteration model (we call the G3 model). The performance of the G3 model with the PCX operator is investigated on three commonly used test problems and is compared with a number of evolutionary and classical optimization algorithms including other real-parameter GAs with the unimodal normal distribution crossover (UNDX) and the simplex crossover (SPX) operators, the correlated self-adaptive evolution strategy, the covariance matrix adaptation evolution strategy (CMA-ES), the differential evolution technique, and the quasi-Newton method. The proposed approach is found to consistently and reliably perform better than all other methods used in the study. A scale-up study with problem sizes up to 500 variables shows a polynomial computational complexity of the proposed approach. This extensive study clearly demonstrates the power of the proposed technique in tackling real-parameter optimization problems.

Solving large parameter optimization problems using genetic algorithms

1995 ◽  
Author(s):  
K Krishnakumar ◽  
R Swaminathan ◽  
S Garg ◽  
S Narayanaswamy
Keyword(s):  
Genetic Algorithms ◽  
Parameter Optimization ◽  
Optimization Problems ◽  
Large Parameter

Evolutionary algorithm characterization in real parameter optimization problems

2013 ◽  
Vol 13 (4) ◽  
pp. 1902-1921 ◽  
Author(s):  
Pilar Caamaño ◽  
Francisco Bellas ◽  
Jose A. Becerra ◽  
Richard J. Duro
Keyword(s):  
Evolutionary Algorithm ◽  
Parameter Optimization ◽  
Optimization Problems ◽  
Real Parameter
Sign in / Sign up

Export Citation Format

Share Document