Adaptive evolutionary algorithms have been widely developed to improve the management of the balance between intensification and diversification during the search. Nevertheless, this balance may need to be dynamically adjusted over time. Based on previous works on adaptive operator selection, we investigate in this paper how an adaptive controller can be used to achieve more dynamic search scenarios and what is the real impact of possible combinations of control components. This study may be helpful for the development of more autonomous and efficient evolutionary algorithms
Selection is a core genetic operator in many evolutionary algorithms (EAs). The performance of EAs o...
The paper concerns the application of Genetic Algorithms and Genetic Programming to complex tasks su...
Adaptation of parameters and operators is one of the most important and promising areas of research ...
In this paper, we investigate how adaptive operator selection techniques are able to efficiently man...
One of the settings that most affect the performance of Evolutionary Algorithms is the selection of ...
In this paper we propose a generic framework for Dynamic Island Models, which can be used as an orig...
Evolutionary algorithms have been efficiently used for solv-ing combinatorial problems. However a su...
Parameters of Evolutionary Algorithms (EAs) greatly influence their ability to produce good results....
Parameter tuning in Evolutionary Algorithms (EA) generally result in suboptimal choices of values be...
This paper presents a method to encapsulate parameters of evolutionary algorithms and to create an a...
International audienceThe performance of an evolutionary algorithm strongly depends on the design of...
We present evidence indicating that adding a crossover island greatly improves the performance of a ...
Evolutionary algorithms are general, randomized search heuristics that are influ-enced by many param...
Evolutionary Algorithms are search algorithms based on the Darwinian metaphor of “Natural Selection”...
Selection is a core genetic operator in many evolutionary algorithms (EAs). The performance of EAs o...
The paper concerns the application of Genetic Algorithms and Genetic Programming to complex tasks su...
Adaptation of parameters and operators is one of the most important and promising areas of research ...
In this paper, we investigate how adaptive operator selection techniques are able to efficiently man...
One of the settings that most affect the performance of Evolutionary Algorithms is the selection of ...
In this paper we propose a generic framework for Dynamic Island Models, which can be used as an orig...
Evolutionary algorithms have been efficiently used for solv-ing combinatorial problems. However a su...
Parameters of Evolutionary Algorithms (EAs) greatly influence their ability to produce good results....
Parameter tuning in Evolutionary Algorithms (EA) generally result in suboptimal choices of values be...
This paper presents a method to encapsulate parameters of evolutionary algorithms and to create an a...
International audienceThe performance of an evolutionary algorithm strongly depends on the design of...
We present evidence indicating that adding a crossover island greatly improves the performance of a ...
Evolutionary algorithms are general, randomized search heuristics that are influ-enced by many param...
Evolutionary Algorithms are search algorithms based on the Darwinian metaphor of “Natural Selection”...
Selection is a core genetic operator in many evolutionary algorithms (EAs). The performance of EAs o...
The paper concerns the application of Genetic Algorithms and Genetic Programming to complex tasks su...
Adaptation of parameters and operators is one of the most important and promising areas of research ...