A FRAMEWORK FOR MEMETIC ALGORITHMS

The term “memetic algorithm” was introduced by Moscato is an extension of the traditional genetic algorithm. It uses a local search technique to reduce the likelihood of the premature convergence. Memetic algorithms are intrinsically concerned with exploiting all available knowledge about the problem under study. MAs are population-based metaheuristics. In this chapter we explore the applications of memetic algorithms to problems within the domains of image processing, data clustering and Graph coloring, i.e., how we can use the memetic algorithms in graph coloring problems, how it can be used in clustering based problems and how it is useful in image processing. Here, we discuss how these algorithms can be used for optimization problems. We conclude by reinforcing the importance of research on the areas of metaheuristics for optimization.

Download Full-text

Memetic Algorithms and Their Applications in Computer Science

Computer Vision ◽

10.4018/978-1-5225-5204-8.ch060 ◽

2018 ◽

pp. 1461-1482 ◽

Cited By ~ 1

Author(s):

B. K. Tripathy ◽

Sooraj T. R. ◽

R. K. Mohanty

Keyword(s):

Genetic Algorithm ◽

Image Processing ◽

Local Search ◽

Computer Science ◽

Graph Coloring ◽

Data Clustering ◽

Memetic Algorithm ◽

Optimization Problems ◽

Memetic Algorithms ◽

Population Based

The term “memetic algorithm” was introduced by Moscato is an extension of the traditional genetic algorithm. It uses a local search technique to reduce the likelihood of the premature convergence. Memetic algorithms are intrinsically concerned with exploiting all available knowledge about the problem under study. MAs are population-based metaheuristics. In this chapter we explore the applications of memetic algorithms to problems within the domains of image processing, data clustering and Graph coloring, i.e., how we can use the memetic algorithms in graph coloring problems, how it can be used in clustering based problems and how it is useful in image processing. Here, we discuss how these algorithms can be used for optimization problems. We conclude by reinforcing the importance of research on the areas of metaheuristics for optimization.

Download Full-text

A new Artificial Bee Colony algorithm for Set Covering Problems

Electrical Engineering and Information Technology ◽

10.2495/ceeit140051 ◽

2014 ◽

Cited By ~ 6

Author(s):

Broderick Crawford ◽

Ricardo Soto ◽

Rodrigo Cuesta Aguilar ◽

Fernando Paredes

Keyword(s):

Artificial Bee Colony Algorithm ◽

Artificial Bee Colony ◽

Set Covering ◽

Covering Problems ◽

Set Covering Problems ◽

Bee Colony

Download Full-text

An Analysis of a KNN Perturbation Operator: An Application to the Binarization of Continuous Metaheuristics

Mathematics ◽

10.3390/math9030225 ◽

2021 ◽

Vol 9 (3) ◽

pp. 225

Author(s):

José García ◽

Gino Astorga ◽

Víctor Yepes

Keyword(s):

Transfer Functions ◽

Optimization Problems ◽

Optimization Methods ◽

Set Covering ◽

Covering Problem ◽

Perturbation Operator ◽

K Nearest Neighbors ◽

Combinatorial Optimization Problems ◽

Box Plots ◽

Metaheuristic Techniques

The optimization methods and, in particular, metaheuristics must be constantly improved to reduce execution times, improve the results, and thus be able to address broader instances. In particular, addressing combinatorial optimization problems is critical in the areas of operational research and engineering. In this work, a perturbation operator is proposed which uses the k-nearest neighbors technique, and this is studied with the aim of improving the diversification and intensification properties of metaheuristic algorithms in their binary version. Random operators are designed to study the contribution of the perturbation operator. To verify the proposal, large instances of the well-known set covering problem are studied. Box plots, convergence charts, and the Wilcoxon statistical test are used to determine the operator contribution. Furthermore, a comparison is made using metaheuristic techniques that use general binarization mechanisms such as transfer functions or db-scan as binarization methods. The results obtained indicate that the KNN perturbation operator improves significantly the results.

Download Full-text