On the Applicability of Diploid Genetic Algorithms in Dynamic Environments

2014 ◽  
Author(s):  
Harsh Bhasin ◽  
Gitanshu Behal ◽  
Nimish Aggarwal ◽  
Raj Kumar Saini ◽  
Shivani Choudhary
Keyword(s):  
Genetic Algorithms ◽  
Dynamic Environments ◽  
Diploid Genetic Algorithms

On the applicability of diploid genetic algorithms in dynamic environments

2015 ◽  
Vol 20 (9) ◽  
pp. 3403-3410 ◽  
Author(s):  
Harsh Bhasin ◽  
Gitanshu Behal ◽  
Nimish Aggarwal ◽  
Raj Kumar Saini ◽  
Shivani Choudhary
Keyword(s):  
Genetic Algorithms ◽  
Dynamic Environments ◽  
Diploid Genetic Algorithms

scholarly journals Genetic Algorithms with Memory- and Elitism-Based Immigrants in Dynamic Environments

2008 ◽  
Vol 16 (3) ◽  
pp. 385-416 ◽  
Author(s):  
Shengxiang Yang
Keyword(s):  
Genetic Algorithms ◽  
Optimization Problems ◽  
Dynamic Environments ◽  
Population Diversity ◽  
Dynamic Problems ◽  
Dynamic Optimization Problems ◽  
Hybrid Memory ◽  
Memory Schemes ◽  
Memory Scheme ◽  
With Memory

In recent years the genetic algorithm community has shown a growing interest in studying dynamic optimization problems. Several approaches have been devised. The random immigrants and memory schemes are two major ones. The random immigrants scheme addresses dynamic environments by maintaining the population diversity while the memory scheme aims to adapt genetic algorithms quickly to new environments by reusing historical information. This paper investigates a hybrid memory and random immigrants scheme, called memory-based immigrants, and a hybrid elitism and random immigrants scheme, called elitism-based immigrants, for genetic algorithms in dynamic environments. In these schemes, the best individual from memory or the elite from the previous generation is retrieved as the base to create immigrants into the population by mutation. This way, not only can diversity be maintained but it is done more efficiently to adapt genetic algorithms to the current environment. Based on a series of systematically constructed dynamic problems, experiments are carried out to compare genetic algorithms with the memory-based and elitism-based immigrants schemes against genetic algorithms with traditional memory and random immigrants schemes and a hybrid memory and multi-population scheme. The sensitivity analysis regarding some key parameters is also carried out. Experimental results show that the memory-based and elitism-based immigrants schemes efficiently improve the performance of genetic algorithms in dynamic environments.

Genetic Algorithms with Immigrants Scheme for Dynamic Optimization Problems

2013 ◽  
Vol 333-335 ◽  
pp. 1379-1383
Author(s):  
Yan Wu ◽  
Xiao Xiong Liu
Keyword(s):  
Genetic Algorithms ◽  
Dynamic Optimization ◽  
Optimization Problems ◽  
Main Idea ◽  
Optimal Solution ◽  
Search Space ◽  
Dynamic Environments ◽  
Dynamic Optimization Problems ◽  
New Space ◽  
Over Time

In dynamic environments, it is difficult to track a changing optimal solution over time. Over the years, many approaches have been proposed to solve the problem with genetic algorithms. In this paper a new space-based immigrant scheme for genetic algorithms is proposed to solve dynamic optimization problems. In this scheme, the search space is divided into two subspaces using the elite of the previous generation and the range of variables. Then the immigrants are generated from both the subspaces and inserted into current population. The main idea of the approach is to increase the diversity more evenly and dispersed. Finally an experimental study on dynamic sphere function was carried out to compare the performance of several genetic algorithms. The experimental results show that the proposed algorithm is effective for the function with moving optimum and can adapt the dynamic environments rapidly.

scholarly journals Adaptive Primal–Dual Genetic Algorithms in Dynamic Environments

2009 ◽  
Vol 39 (6) ◽  
pp. 1348-1361 ◽  
Author(s):  
Hongfeng Wang ◽  
Shengxiang Yang ◽  
W.H. Ip ◽  
Dingwei Wang
Keyword(s):  
Genetic Algorithms ◽  
Dynamic Environments ◽  
Primal Dual
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