In this paper, we investigate how adaptive operator selection techniques are able to efficiently manage the balance between exploration and exploitation in an evolutionary algorithm, when solving combinatorial optimization problems. We introduce new high level reactive search strategies based on a generic algorithm’s controller that is able to schedule the basic variation operators of the evolutionary algorithm, according to the observed state of the search. Our experiments on SAT instances show that reactive search strategies improve the performance of the solving algorithm
Parameter tuning in Evolutionary Algorithms (EA), is a great obstacle that can become the key to suc...
Parameters of Evolutionary Algorithms (EAs) greatly influence their ability to produce good results....
In this paper two methods for evolutionary algorithm control are proposed. The first one is a new me...
International audienceIn this paper, we investigate how adaptive operator selection techniques are a...
Adaptive evolutionary algorithms have been widely developed to improve the management of the balance...
International audienceThe performance of an evolutionary algorithm strongly depends on the design of...
The paper concerns the application of Genetic Algorithms and Genetic Programming to complex tasks su...
One of the settings that most affect the performance of Evolutionary Algorithms is the selection of ...
International audienceThis paper presents a method to encapsulate parameters of evolutionary algorit...
In this thesis we investigate how intelligent techniques, such as Evolutionary Algorithms, can be ap...
Parameter tuning in Evolutionary Algorithms (EA) generally result in suboptimal choices of values be...
Date du colloque : 10/2009International audienceEvolutionary algorithms have been efficiently u...
Selection is a core genetic operator in many evolutionary algorithms (EAs). The performance of EAs o...
In solving problems with evolutionary algorithms (EAs), the performance of the EA will be affected b...
Abstract- Many real-world applications involve complex optimization problem with various competing s...
Parameter tuning in Evolutionary Algorithms (EA), is a great obstacle that can become the key to suc...
Parameters of Evolutionary Algorithms (EAs) greatly influence their ability to produce good results....
In this paper two methods for evolutionary algorithm control are proposed. The first one is a new me...
International audienceIn this paper, we investigate how adaptive operator selection techniques are a...
Adaptive evolutionary algorithms have been widely developed to improve the management of the balance...
International audienceThe performance of an evolutionary algorithm strongly depends on the design of...
The paper concerns the application of Genetic Algorithms and Genetic Programming to complex tasks su...
One of the settings that most affect the performance of Evolutionary Algorithms is the selection of ...
International audienceThis paper presents a method to encapsulate parameters of evolutionary algorit...
In this thesis we investigate how intelligent techniques, such as Evolutionary Algorithms, can be ap...
Parameter tuning in Evolutionary Algorithms (EA) generally result in suboptimal choices of values be...
Date du colloque : 10/2009International audienceEvolutionary algorithms have been efficiently u...
Selection is a core genetic operator in many evolutionary algorithms (EAs). The performance of EAs o...
In solving problems with evolutionary algorithms (EAs), the performance of the EA will be affected b...
Abstract- Many real-world applications involve complex optimization problem with various competing s...
Parameter tuning in Evolutionary Algorithms (EA), is a great obstacle that can become the key to suc...
Parameters of Evolutionary Algorithms (EAs) greatly influence their ability to produce good results....
In this paper two methods for evolutionary algorithm control are proposed. The first one is a new me...