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AEI
2005
99views more  AEI 2005»
14 years 9 months ago
Comparison among five evolutionary-based optimization algorithms
Evolutionary algorithms (EAs) are stochastic search methods that mimic the natural biological evolution and/or the social behavior of species. Such algorithms have been developed ...
Emad Elbeltagi, Tarek Hegazy, Donald E. Grierson
GECCO
2005
Springer
196views Optimization» more  GECCO 2005»
15 years 2 months ago
Breeding swarms: a new approach to recurrent neural network training
This paper shows that a novel hybrid algorithm, Breeding Swarms, performs equal to, or better than, Genetic Algorithms and Particle Swarm Optimizers when training recurrent neural...
Matthew Settles, Paul Nathan, Terence Soule
GECCO
2007
Springer
155views Optimization» more  GECCO 2007»
15 years 3 months ago
Observing the swarm behaviour during its evolutionary design
Evolutionary Algorithms (EAs) can be used for designing Particle Swarm Optimization (PSO) algorithms that work, in some cases, considerably better than the human-designed ones. By...
Laura Diosan, Mihai Oltean
EUROGP
2005
Springer
117views Optimization» more  EUROGP 2005»
15 years 3 months ago
Extending Particle Swarm Optimisation via Genetic Programming
Abstract. Particle Swarm Optimisers (PSOs) search using a set of interacting particles flying over the fitness landscape. These are typically controlled by forces that encourage ...
Riccardo Poli, William B. Langdon, Owen Holland
GECCO
2008
Springer
161views Optimization» more  GECCO 2008»
14 years 10 months ago
A new quantum behaved particle swarm optimization
This paper presents a variant of Quantum behaved Particle Swarm Optimization (QPSO) named Q-QPSO for solving global optimization problems. The Q-QPSO algorithm is based on the cha...
Millie Pant, Radha Thangaraj, Ajith Abraham