scholarly journals Genetic Algorithm And Memetic Algorithm On Graph Coloring

2014 ◽  
Vol 1 (12) ◽  
Keyword(s):  
Genetic Algorithm ◽  
Graph Coloring ◽  
Memetic Algorithm

Memetic Algorithms and Their Applications in Computer Science

2018 ◽  
pp. 73-93
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.

Memetic Algorithms and Their Applications in Computer Science

2018 ◽  
pp. 1461-1482 ◽  
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.

Memetic Algorithm Approach to Two-Stage Hybrid Flow Shop Scheduling Problem with Identical Parallel Machines

2013 ◽  
Vol 651 ◽  
pp. 548-552
Author(s):  
Parinya Kaweegitbundit
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Parallel Machines ◽  
Flow Shop ◽  
Memetic Algorithm ◽  
Flow Shop Scheduling ◽  
Hybrid Flow Shop ◽  
Scheduling Problems ◽  
Two Stage ◽  
Hybrid Flow Shop Scheduling

This paper considers two stage hybrid flow shop (HFS) with identical parallel machine. The objectives is to determine makespan have been minimized. This paper presented memetic algorithm procedure to solve two stage HFS problems. To evaluated performance of propose method, the results have been compared with two meta-heuristic, genetic algorithm, simulated annealing. The experimental results show that propose method is more effective and efficient than genetic algorithm and simulated annealing to solve two stage HFS scheduling problems.

scholarly journals Penjadwalan Tenaga Kebidanan Menggunakan Algoritma Memetika

2018 ◽  
Vol 8 (1) ◽  
pp. 99
Author(s):  
A. Y. Erwin Dodu ◽  
Deny Wiria Nugraha ◽  
Subkhan Dinda Putra
Keyword(s):  
Genetic Algorithm ◽  
Local Search ◽  
Public Hospital ◽  
Memetic Algorithm ◽  
Optimal Schedule ◽  
Scheduling System ◽  
Local Search Methods ◽  
Evolution Algorithm ◽  
Central Sulawesi

The problem of midwife scheduling is one of the most frequent problems in hospitals. Midwife should be available 24 hours a day for a full week to meet the needs of the patient. Therefore, good or bad midwife scheduling result will have an impact on the quality of care on the patient and the health of the midwife on duty. The midwife scheduling process requires a lot of time, effort and good cooperation between some parties to solve this problem that is often faced by the Regional Public Hospital Undata Palu Central Sulawesi Province. This research aimed to apply Memetics algorithm to make scheduling system of midwifery staff at Regional Public Hospital Undata Palu Central Sulawesi Province that can facilitate the process of midwifery scheduling as well as to produce optimal schedule. The scheduling system created will follow the rules and policies applicable in the hospital and will also pay attention to the midwife's preferences on how to schedule them according to their habits and needs. Memetics algorithm is an optimization algorithm that combines Evolution Algorithm  and Local Search method. Evolution Algorithm in Memetics Algorithm generally refers to Genetic Algorithm so that the characteristics of Memetics Algotihm are identical with  Genetic Algorithm characteristics with the addition of Local Search methods. Local Search in Memetic Algorithm aims to improve the quality of an individual so it is expected to accelerate the time to get a solution.

scholarly journals Motion Planning Using a Memetic Evolution Algorithm for Swarm Robots

10.5772/45669 ◽  
2012 ◽  
Vol 9 (1) ◽  
pp. 19 ◽  
Author(s):  
Chien-Chou Lin ◽  
Kun-Cheng Chen ◽  
Wei-Ju Chuang
Keyword(s):  
Genetic Algorithm ◽  
Formation Control ◽  
Memetic Algorithm ◽  
Initial Population ◽  
Local Motion ◽  
Computation Efficiency ◽  
Swarm Robots ◽  
Evolution Algorithm ◽  
Simulation Results ◽  
Path Planner

A hierarchical memetic algorithm (MA) is proposed for the path planning and formation control of swarm robots. The proposed algorithm consists of a global path planner (GPP) and a local motion planner (LMP). The GPP plans a trajectory within the Voronoi diagram (VD) of the free space. An MA with a non-random initial population plans a series of configurations along the path given by the former stage. The MA locally adjusts the robot positions to search for better fitness along the gradient direction of the distance between the swarm robots and the intermediate goals (IGs). Once the optimal configuration is obtained, the best chromosomes are reserved as the initial population for the next generation. Since the proposed MA has a non-random initial population and local searching, it is more efficient and the planned path is faster compared to a traditional genetic algorithm (GA). The simulation results show that the proposed algorithm works well in terms of path smoothness and computation efficiency.

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