Noise pressure: systematic overestimation of population fitness in genetic algorithms with noisy fitness functions.
Timothy MeekhofTerence SoulePublished in: GECCO (2010)
Keyphrases
- fitness function
- genetic algorithm
- initial population
- crossover and mutation
- evolutionary algorithm
- genetic programming
- evolutionary process
- noisy data
- mutation operator
- candidate solutions
- population size
- evolutionary computation
- genetic algorithm ga
- noise free
- noisy environments
- genetic operators
- crossover operator
- fitness landscape
- population diversity
- multi population
- fitness evaluation
- adaptive parameter control
- search space
- low signal to noise ratio
- missing data
- simulated annealing
- noise reduction
- evolutionary search
- differential evolution
- genetic algorithm is employed
- multi objective
- evolutionary strategy
- neural network
- artificial neural networks
- evolution process
- multi objective evolutionary algorithms
- constrained optimization problems
- particle swarm
- natural selection
- optimization problems
- mutation probability
- optimization method
- selection operator
- hybrid algorithm
- multi objective optimization
- search algorithm
- nsga ii
- particle swarm optimization pso
- test data generation
- gaussian noise
- fuzzy logic
- premature convergence
- metaheuristic
- optimization algorithm