Sizing Populations for Serial and Parallel Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Adaptively Resizing Populations: An Algorithm and Analysis
Proceedings of the 5th International Conference on Genetic Algorithms
Parameter-Free Genetic Algorithm Inspired by ``Disparity Theory of Evolution''
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
ICECT'03 Proceedings of the third international conference on Engineering computational technology
The micro genetic algorithm 2: towards online adaptation in evolutionary multiobjective optimization
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
IEEE Transactions on Evolutionary Computation
MAGMA: a multiagent architecture for metaheuristics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A multiagent genetic algorithm for global numerical optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Optimizing communications in vehicular ad hoc networks using evolutionary computation and simulation
CSTST '08 Proceedings of the 5th international conference on Soft computing as transdisciplinary science and technology
CSTST '08 Proceedings of the 5th international conference on Soft computing as transdisciplinary science and technology
Using an evolutionary algorithm to optimize the broadcasting methods in mobile ad hoc networks
Journal of Network and Computer Applications
Adaptive multi-objective genetic algorithm using multi-pareto-ranking
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Introduction of a combination vector to optimise the interpolation of numerical phantoms
Expert Systems with Applications: An International Journal
Hi-index | 0.00 |
This paper presents a self-adaptive algorithm that hybridises evolutionary and multiagent concepts. Each evolutionary individual is implemented as a simple agent capable of re-production and predation. The transitions between these two states depend on the agent's local environment. Thus, no explicit global process is defined to select neither the mates nor the preys. The convergence of the algorithm emerges from the behaviour of the agents. This brings interesting properties, such as population size self-regulation. Two sets of experimental results are provided: a comparison with Saw-Tooth Algorithm and micro-GA using four classical functions and an optimisation of the efficiency and the weight of an electrical motor. Some possible evolutions and prospects are finally proposed.