scholarly journals Adaptive Genetic Algorithm and Quasi-parallel Genetic Algorithm: Application to Knapsack Problem

2006 ◽  
pp. 189-196 ◽  
Author(s):  
Kwok Yip Szeto ◽  
Jian Zhang
Keyword(s):  
Genetic Algorithm ◽  
Knapsack Problem ◽  
Adaptive Genetic Algorithm ◽  
Parallel Genetic Algorithm

Solving the 0/1 knapsack problem using an adaptive genetic algorithm

2002 ◽  
Vol 16 (1) ◽  
pp. 23-30 ◽  
Author(s):  
ZOHEIR EZZIANE
Keyword(s):  
Genetic Algorithm ◽  
Knapsack Problem ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Adaptive Genetic Algorithm ◽  
Stochastic Algorithms ◽  
Initial Population ◽  
Genetic Operators ◽  
Adaptive Penalty ◽  
Genetic Algorithm Approach

Probabilistic and stochastic algorithms have been used to solve many hard optimization problems since they can provide solutions to problems where often standard algorithms have failed. These algorithms basically search through a space of potential solutions using randomness as a major factor to make decisions. In this research, the knapsack problem (optimization problem) is solved using a genetic algorithm approach. Subsequently, comparisons are made with a greedy method and a heuristic algorithm. The knapsack problem is recognized to be NP-hard. Genetic algorithms are among search procedures based on natural selection and natural genetics. They randomly create an initial population of individuals. Then, they use genetic operators to yield new offspring. In this research, a genetic algorithm is used to solve the 0/1 knapsack problem. Special consideration is given to the penalty function where constant and self-adaptive penalty functions are adopted.

An Adaptive Genetic Algorithm Based on Multi-Population Parallel Evolutionary for Highway Alignment Optimization Model

2011 ◽  
Vol 58-60 ◽  
pp. 1499-1503 ◽  
Author(s):  
Jian Xin Chen ◽  
Yong Yi Guo ◽  
Mai Xia Lv
Keyword(s):  
Genetic Algorithm ◽  
Convergence Rate ◽  
Single Species ◽  
Adaptive Genetic Algorithm ◽  
Parallel Genetic Algorithm ◽  
Single Population ◽  
Highway Design ◽  
Population Evolution ◽  
Highway Alignment Optimization ◽  
Highway Alignment

Based on the characteristics of the highway design, this paper transfers all the factors involved in the highway design to a cost-optimized-oriented model and designs a variety parallel genetic algorithm to optimize highway design. While maintaining evolution stability of excellent individual, the algorithm can improve convergence rate and accuracy and avoid premature convergence generated by single-population evolution. To some extent, it makes up generalization-lacking defects of a single species or steady parameters in premature overcoming. Finally, the algorithm is verified with a good result. This algorithm provides a useful method for highway design.

Recycling Lethal Chromosomes Based on Immune Operation in Genetic Algorithm for Multi-Knapsack Problem

2014 ◽  
Vol 1 ◽  
pp. 219-222
Author(s):  
Jing Guo ◽  
Jousuke Kuroiwa ◽  
Hisakazu Ogura ◽  
Izumi Suwa ◽  
Haruhiko Shirai ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Knapsack Problem
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