Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
Swarm intelligence
Handbook of Neural Networks for Speech Processing
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Genetic Programming III: Darwinian Invention & Problem Solving
Genetic Programming III: Darwinian Invention & Problem Solving
Crossover in Grammatical Evolution
Genetic Programming and Evolvable Machines
Genetic Programming IV: Routine Human-Competitive Machine Intelligence
Genetic Programming IV: Routine Human-Competitive Machine Intelligence
A new study in maintenance for transmission lines
WSEAS Transactions on Circuits and Systems
WSEAS Transactions on Circuits and Systems
A theoretical and empirical analysis of convergence related particle swarm optimization
WSEAS Transactions on Systems and Control
IEEE Transactions on Evolutionary Computation
Comprehensive learning particle swarm optimizer for global optimization of multimodal functions
IEEE Transactions on Evolutionary Computation
Artificial bee colony algorithm with multiple onlookers for constrained optimization problems
ECC'11 Proceedings of the 5th European conference on European computing conference
Guided artificial bee colony algorithm
ECC'11 Proceedings of the 5th European conference on European computing conference
AMERICAN-MATH'12/CEA'12 Proceedings of the 6th WSEAS international conference on Computer Engineering and Applications, and Proceedings of the 2012 American conference on Applied Mathematics
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There exists a clear difference between cooperative and competitive strategies. The former ones are based on the swarm colonies, in which all individuals share its knowledge about the goal in order to pass such information to other individuals to get optimum solution. The latter ones are based on genetic models, that is, individuals can die and new individuals are created combining information of alive one; or are based on molecular/celular behaviour passing information from one structure to another. A Grammatical Swarm model is applied to obtain the Neural Network topology of a given problem, training the net with a Particle Swarm algorithm in R. This paper just shows some ideas in order to obtain an automatic way to define the most suitable neural network topology for a given patter set. High dimension problem is also mentioned when dealing with the particle swarm algorithm.