Paper Info
Title
Inheriting Parents Operators: A New Dynamic Strategy for Improving Evolutionary Algorithms
Abstract
Our research has been focused on developing techniques for solving binary constraint satisfaction problems (CSP) using evolutionary algorithms, which take into account the constraint graphs topology. In this paper, we introduce a new idea to improve the performance of evolutionary algorithms, that solve complex problems. It is inspired from a real world observation: The ability to evolve for an individual in an environment that changes is not only related to its genetic material. It also comes from what has learned from it parents. The key idea of this paper is to use its inheritance to dynamically improve the way the algorithm creates a new population using a given set of operators. This new dynamic operator selection strategy has been applied to an evolutionary algorithm to solve CSPs, but can be easily extended to other class of evolutionary algorithms. A set of benchmarks shows how the new strategy can help to solve large NP-hard problems with the 3-graph coloring example.
Year
DOI
Venue
2002
10.1007/3-540-48050-1_37
ISMIS
Keywords
Field
DocType
new dynamic strategy,improving evolutionary algorithms,binary constraint satisfaction problem,constraint graphs topology,new strategy,3-graph coloring example,key idea,new population,new dynamic operator selection,evolutionary algorithm,inheriting parents operators,new idea,complex problem,np hard problem,genetics,constraint satisfaction problem,graph coloring
Constraint satisfaction,Evolutionary algorithm,Computer science,Evolutionary computation,Constraint satisfaction problem,Artificial intelligence,Evolutionary programming,Evolutionary music,Genetic algorithm,Binary constraint
Conference
Volume
ISSN
ISBN
2366
0302-9743
3-540-43785-1
Citations 
PageRank 
References 
9
0.63
10
Authors
2
Name
Order
Citations
PageRank
María Cristina Riff120023.91
Xavier Bonnaire28511.88