scholarly journals Genetic algorithms and scatter search: unsuspected potentials

1994 ◽  
Vol 4 (2) ◽  
Author(s):  
Fred Glover
Keyword(s):  
Genetic Algorithms ◽  
Scatter Search

Scatter Search and Genetic Algorithms for MAX-SAT Problems

2008 ◽  
Vol 7 (2) ◽  
pp. 101-124 ◽  
Author(s):  
Dalila Boughaci ◽  
Belaïd Benhamou ◽  
Habiba Drias
Keyword(s):  
Genetic Algorithms ◽  
Scatter Search ◽  
Max Sat

Metaheuristics in Data Mining

2011 ◽  
pp. 1200-1206
Author(s):  
Miguel García Torres
Keyword(s):  
Neural Networks ◽  
Genetic Algorithms ◽  
High Performance ◽  
Optimization Problems ◽  
Scatter Search ◽  
Optimization Methods ◽  
Population Based ◽  
Variable Neighbourhood Search ◽  
Neighbourhood Search ◽  
Distribution Algorithms

The Metaheuristics are general strategies for designing heuristic procedures with high performance. The term metaheuristic, which appeared in 1986 for the first time (Glover, 1986), is compound by the terms: “meta”, that means over or behind, and “heuristic”. Heuristic is the qualifying used for methods of solving optimization problems that are obtained from the intuition, expertise or general knowledge (Michalewicz & Fogel, 2000). Nowadays a lot of known strategies can be classified as metaheuristics and there are a clear increasing number of research papers and applications that use this kind of methods. Several optimization methods that already existed when the term appeared have been later interpreted as metaheuristics (Glover & Kochenberger, 2003). Genetic Algorithms, Neural Networks, Local Searches, and Simulated Annealing are some of those classical metaheuristics. Several modern metaheuristics have succeeded in solving relevant optimization problems in industry, business and engineering. The most relevant among them are Tabu Search, Variable Neighbourhood Search and GRASP. New population based evolutionary metaheuristics such as Scatter Search and Estimation Distribution Algorithms are also quite important. Besides Neural Networks and Genetic Algorithms, other nature-inspired metaheuristics such as Ant Colony Optimization and Particle Swarm Optimization are also now well known metaheuristics.

Parameterized Schemes of Metaheuristics: Basic Ideas and Applications With Genetic Algorithms, Scatter Search, and GRASP

2013 ◽  
Vol 43 (3) ◽  
pp. 570-586 ◽  
Author(s):  
Francisco Almeida ◽  
Domingo Gimenez ◽  
Jose Juan Lopez-Espin ◽  
Melquiades Perez-Perez
Keyword(s):  
Genetic Algorithms ◽  
Scatter Search ◽  
Basic Ideas

Genetic Algorithms—a Tool for OR?

1996 ◽  
Vol 47 (4) ◽  
pp. 550-561 ◽  
Author(s):  
Kathryn A Dowsland
Keyword(s):  
Genetic Algorithms

Enhanced Artificial Intelligent Method for Representing the Photonic Crystal Fiber Profile Index

2018 ◽  
Vol 1 (1) ◽  
pp. 2-19
Author(s):  
Mahmood Sh. Majeed ◽  
Raid W. Daoud
Keyword(s):  
Genetic Algorithms ◽  
Photonic Crystal ◽  
Photonic Crystal Fiber ◽  
New Method ◽  
Proper Solution ◽  
Artificial Intelligent ◽  
Crystal Fiber ◽  
Fitness Value ◽  
First Pass ◽  
Crossover And Mutation

A new method proposed in this paper to compute the fitness in Genetic Algorithms (GAs). In this new method the number of regions, which assigned for the population, divides the time. The fitness computation here differ from the previous methods, by compute it for each portion of the population as first pass, then the second pass begin to compute the fitness for population that lye in the portion which have bigger fitness value. The crossover and mutation and other GAs operator will do its work only for biggest fitness portion of the population. In this method, we can get a suitable and accurate group of proper solution for indexed profile of the photonic crystal fiber (PCF).

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