A Genetic Algorithm for Optimization of a Relational Knapsack Problem with Respect to a Description Logic Knowledge Base

2011 ◽  
pp. 201-206
Author(s):  
Thomas Fischer ◽  
Johannes Ruhland
Keyword(s):  
Genetic Algorithm ◽  
Knowledge Base ◽  
Knapsack Problem ◽  
Description Logic

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

scholarly journals Knowledge Representation of Highly Dynamic Ontologies Using Defeasible Logic

2021 ◽  
Vol 10 (2) ◽  
pp. 948-954
Keyword(s):  
Computational Complexity ◽  
Knowledge Representation ◽  
Knowledge Base ◽  
Description Logic ◽  
Defeasible Logic ◽  
Small Domain ◽  
Automated Support ◽  
Logical Rules

Description logic gives us the ability of reasoning with acceptable computational complexity with retaining the power of expressiveness. The power of description logic can be accompanied by the defeasible logic to manage non-monotonic reasoning. In some domains, we need flexible reasoning and knowledge representation to deal the dynamicity of such domains. In this paper, we present a DL representation for a small domain that describes the connections between different entities in a university publication system to show how could we deal with changeability in domain rules. An automated support can be provided on the basis of defeasible logical rules to represent the typicality in the knowledge base and to solve the conflicts that might happen.

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.

Genetic Programming over Context-Free Languages with Linear Constraints for the Knapsack Problem: First Results

2002 ◽  
Vol 10 (1) ◽  
pp. 51-74 ◽  
Author(s):  
Peter Bruhn ◽  
Andreas Geyer-Schulz
Keyword(s):  
Genetic Algorithm ◽  
Combinatorial Optimization ◽  
Genetic Programming ◽  
Knapsack Problem ◽  
Linear Constraints ◽  
Penalty Functions ◽  
Multidimensional Knapsack Problem ◽  
Multidimensional Knapsack ◽  
First Results ◽  
Context Free

In this paper, we introduce genetic programming over context-free languages with linear constraints for combinatorial optimization, apply this method to several variants of the multidimensional knapsack problem, and discuss its performance relative to Michalewicz's genetic algorithm with penalty functions. With respect to Michalewicz's approach, we demonstrate that genetic programming over context-free languages with linear constraints improves convergence. A final result is that genetic programming over context-free languages with linear constraints is ideally suited to modeling com-plementarities between items in a knapsack problem: The more complementarities in the problem, the stronger the performance in comparison to its competitors.

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