Screen confronting Two-Pair Primers from typical Genetic Algorithm to improved local search-based Genetic Algorithm

2014 ◽  
pp. 439-450
Keyword(s):  
Genetic Algorithm ◽  
Local Search

A hybrid genetic algorithm with local search

2001 ◽  
Vol 59 (1-2) ◽  
pp. 107-120 ◽  
Author(s):  
G Vivó-Truyols ◽  
J.R Torres-Lapasió ◽  
A Garrido-Frenich ◽  
M.C Garcı́a-Alvarez-Coque
Keyword(s):  
Genetic Algorithm ◽  
Local Search ◽  
Hybrid Genetic Algorithm

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.

A hybrid genetic algorithm for multiobjective problems with activity analysis-based local search

2009 ◽  
Vol 193 (1) ◽  
pp. 195-203 ◽  
Author(s):  
Gerald Whittaker ◽  
Remegio Confesor ◽  
Stephen M. Griffith ◽  
Rolf Färe ◽  
Shawna Grosskopf ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Local Search ◽  
Hybrid Genetic Algorithm ◽  
Activity Analysis ◽  
Multiobjective Problems

scholarly journals Two meta-heuristics for solving the multi-vehicle multi-covering tour problem with a constraint on the number of vertices

2020 ◽  
pp. 14-14
Author(s):  
Manel Kammoun ◽  
Houda Derbel ◽  
Bassem Jarboui
Keyword(s):  
Genetic Algorithm ◽  
Local Search ◽  
Variable Neighborhood Search ◽  
Search Algorithm ◽  
Descent Method ◽  
Computational Experiments ◽  
Neighborhood Search ◽  
Variable Neighborhood Descent ◽  
Covering Tour Problem ◽  
Neighborhood Search Algorithm

In this work we deal with a generalized variant of the multi-vehicle covering tour problem (m-CTP). The m-CTP consists of minimizing the total routing cost and satisfying the entire demand of all customers, without the restriction of visiting them all, so that each customer not included in any route is covered. In the m-CTP, only a subset of customers is visited to fulfill the total demand, but a restriction is put on the length of each route and the number of vertices that it contains. This paper tackles a generalized variant of the m-CTP, called the multi-vehicle multi-covering Tour Problem (mm-CTP), where a vertex must be covered several times instead of once. We study a particular case of the mm-CTP considering only the restriction on the number of vertices in each route and relaxing the constraint on the length (mm-CTP-p). A hybrid metaheuristic is developet by combining Genetic Algorithm (GA), Variable Neighborhood Descent method (VND), and a General Variable Neighborhood Search algorithm (GVNS) to solve the problem. Computational experiments show that our approaches are competitive with the Evolutionary Local Search (ELS) and Genetic Algorithm (GA), the methods proposed in the literature.

Index Fund Optimization Using a Genetic Algorithm and a Heuristic Local Search

2008 ◽  
Vol 128 (3) ◽  
pp. 407-415 ◽  
Author(s):  
Yukiko Orito ◽  
Manabu Inoguchi ◽  
Hisashi Yamamoto
Keyword(s):  
Genetic Algorithm ◽  
Local Search

scholarly journals A Modified Genetic Algorithm with Local Search Strategies and Multi-Crossover Operator for Job Shop Scheduling Problem

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5440 ◽  
Author(s):  
Monique Simplicio Viana ◽  
Orides Morandin Junior ◽  
Rodrigo Colnago Contreras
Keyword(s):  
Genetic Algorithm ◽  
Local Search ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Crossover Operator ◽  
Local Optima ◽  
Shop Scheduling ◽  
Solution Methods

It is not uncommon for today’s problems to fall within the scope of the well-known class of NP-Hard problems. These problems generally do not have an analytical solution, and it is necessary to use meta-heuristics to solve them. The Job Shop Scheduling Problem (JSSP) is one of these problems, and for its solution, techniques based on Genetic Algorithm (GA) form the most common approach used in the literature. However, GAs are easily compromised by premature convergence and can be trapped in a local optima. To address these issues, researchers have been developing new methodologies based on local search schemes and improvements to standard mutation and crossover operators. In this work, we propose a new GA within this line of research. In detail, we generalize the concept of a massive local search operator; we improved the use of a local search strategy in the traditional mutation operator; and we developed a new multi-crossover operator. In this way, all operators of the proposed algorithm have local search functionality beyond their original inspirations and characteristics. Our method is evaluated in three different case studies, comprising 58 instances of literature, which prove the effectiveness of our approach compared to traditional JSSP solution methods.

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