A Statistical Comparison Study between Genetic Algorithms and Bayesian Optimization Algorithms

Genetic Algorithms and Statistical Methods: A Comparison

Comparison between Genetic Algorithms and Particle Swarm Optimization

This paper compares two evolutionary computation paradigms: genetic algorithms and particle swarm optimization. The operators of each paradigm are reviewed, focusing on how each affects search behavior in the problem space. The goals of the paper are to provide additional insights into how each paradigm works, and to suggest ways in which performance might be improved by incorporating features ...

Relationship between Genetic Algorithms and Ant Colony Optimization Algorithms

Genetic Algorithms (GAs) were introduced by Holland as a computational analogy of adaptive systems. GAs are search procedures based on the mechanics of natural selection and natural genetics. Ant Colony Optimization (ACO) is a metaheuristic inspired by the foraging behavior of ant colonies. ACO was introduced by Dorigo and has evolved significantly in the last few years. Both algorithms have sh...

Statistical Methodology for Quality Comparison between Algorithms

Performance on the quality of the responses obtained by two algorithms, it is not something to be taken lightly. Most of the tests used, are based on the calculation of average, however, this statistic can be affected by extreme results. In addition, a mean difference may be statistically insignificant in practical terms can be decisive. In this paper we propose a statistical test that solved t...

A Hybrid Algorithm using Firefly, Genetic, and Local Search Algorithms

In this paper, a hybrid multi-objective algorithm consisting of features of genetic and firefly algorithms is presented. The algorithm starts with a set of fireflies (particles) that are randomly distributed in the solution space; these particles converge to the optimal solution of the problem during the evolutionary stages. Then, a local search plan is presented and implemented for searching s...