Parameter Optimization Using Covariance Matrix Adaptation - Evolutionary Strategy (CMA-ES), an Approach to Investigate Differences in Channel Properties Between Neuron Subtypes.
Zbigniew Jedrzejewski-SzmekKarina P. AbrahaoJoanna Jedrzejewska-SzmekDavid M. LovingerKim T. BlackwellPublished in: Frontiers Neuroinformatics (2018)
Keyphrases
- cma es
- evolution strategy
- parameter optimization
- differential evolution
- evolutionary strategy
- covariance matrix
- evolutionary algorithm
- convergence speed
- genetic algorithm
- optimization algorithm
- mutation operator
- particle swarm optimization
- particle swarm optimization pso
- evolutionary computation
- genetic algorithm ga
- neural network
- optimization method
- multi objective optimization
- sample size
- principal component analysis
- optimization problems
- parameter settings
- genetic operators
- machine learning
- global search
- crossover operator
- multi objective
- pid controller
- simulated annealing
- candidate solutions
- metaheuristic