A connectionist machine for genetic hillclimbing
A connectionist machine for genetic hillclimbing
Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Intelligence through simulated evolution: forty years of evolutionary programming
Intelligence through simulated evolution: forty years of evolutionary programming
System Identification through Simulated Evolution: A Machine Learning Approach to Modeling
System Identification through Simulated Evolution: A Machine Learning Approach to Modeling
Comparison between Genetic Algorithms and Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Evolutionary Optimization Versus Particle Swarm Optimization: Philosophy and Performance Differences
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
Breeding swarms: a GA/PSO hybrid
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Co-evolutionary particle swarm optimization to solve min-max problems
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
An overview of evolutionary algorithms for parameter optimization
Evolutionary Computation
An improved GA and a novel PSO-GA-based hybrid algorithm
Information Processing Letters
Evolutionary programming techniques for constrained optimizationproblems
IEEE Transactions on Evolutionary Computation
Evolutionary programming made faster
IEEE Transactions on Evolutionary Computation
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
A hybrid of genetic algorithm and particle swarm optimization for recurrent network design
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Application of particle swarm optimization in epileptic spike EEG source localization
Applied Soft Computing
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This paper proposes a novel hybrid algorithm of Particle Swarm Optimization (PSO) and Evolutionary Programming (EP), named Particle Swarm Optimization driven by Evolving Elite Group (PSO-EEG) algorithm. The hybrid algorithm combines the movement update property of canonical PSO with the evolutionary characteristics of EP. It is processed in two stages; elite group stage by EP and ordinary group stage by PSO. For the former group, a novel concept of Evolving Elite Group (EEG) is introduced, which consists of relatively superior particles in a population. The elite particles are evolved by mutation and selection scheme of EP. The other ordinary particles refer to the closest elite particle as well as the global best and the personal best, to update their location. Simulation results demonstrate the proposed PSO-EEG is highly competitive in terms of robustness, accuracy and convergence speed for five well-known complex test functions.