Abstract — Evolutionary algorithms (EAs) are a range of problem-solving techniques based on mechanisms inspired by biological evolution. Nowadays, EAs have proven their ability and effectiveness to solve combinatorial problems. However, these methods require a considerable time of calculation. To overcome this problem, several parallelization strategies have been proposed in the literature. In this paper, we present a new parallel agent-based EC framework for solving numerical optimization problems in order to optimize computation time and solutions quality
This paper presents a novel agent-based simheuristic (ABSH) approach that combines simheuristic and ...
This chapter can be accessed from the link below - Copyright @ 2010 Springer-VerlagAgent-based Evolu...
International audienceThis paper describes a unified view of parallel evolutionary algorithms for mu...
Evolutionary algorithms (EAs) are a range of problem-solving techniques based on mechanisms inspired...
Computing applications such as metaheuristics-based optimization can greatly benefit from multi-core...
The paper presents an analysis of the use of optimization algorithms in parallel solutions and distr...
Evolutionary algorithms (EAs) are stochastic optimization techniques based on the principles of natu...
Computing applications such as metaheuristics-based optimization can greatly benefit from multi-core...
Evolution algorithms for combinatorial optimization have been proposed in the 70's. They did not hav...
AbstractHybridizing agent-based paradigm with evolutionary computation can enhance the field of meta...
Despite all the appealing features of Evolutionary Algorithms (EAs), thousands of calls to the analy...
Supplementary data associated with this article can be found, in the online version, at http://dx.do...
Global Optimization using evolutionary computation (Ee) techniques can perform very well on some pro...
A kind of parallel genetic algorithm based on the idea of multi-agent cooperation was described. The...
Evolutionary algorithms often need huge running times when solving large-scale optimization problems...
This paper presents a novel agent-based simheuristic (ABSH) approach that combines simheuristic and ...
This chapter can be accessed from the link below - Copyright @ 2010 Springer-VerlagAgent-based Evolu...
International audienceThis paper describes a unified view of parallel evolutionary algorithms for mu...
Evolutionary algorithms (EAs) are a range of problem-solving techniques based on mechanisms inspired...
Computing applications such as metaheuristics-based optimization can greatly benefit from multi-core...
The paper presents an analysis of the use of optimization algorithms in parallel solutions and distr...
Evolutionary algorithms (EAs) are stochastic optimization techniques based on the principles of natu...
Computing applications such as metaheuristics-based optimization can greatly benefit from multi-core...
Evolution algorithms for combinatorial optimization have been proposed in the 70's. They did not hav...
AbstractHybridizing agent-based paradigm with evolutionary computation can enhance the field of meta...
Despite all the appealing features of Evolutionary Algorithms (EAs), thousands of calls to the analy...
Supplementary data associated with this article can be found, in the online version, at http://dx.do...
Global Optimization using evolutionary computation (Ee) techniques can perform very well on some pro...
A kind of parallel genetic algorithm based on the idea of multi-agent cooperation was described. The...
Evolutionary algorithms often need huge running times when solving large-scale optimization problems...
This paper presents a novel agent-based simheuristic (ABSH) approach that combines simheuristic and ...
This chapter can be accessed from the link below - Copyright @ 2010 Springer-VerlagAgent-based Evolu...
International audienceThis paper describes a unified view of parallel evolutionary algorithms for mu...