Threshold selecting: best possible probability distribution for crossover selection in genetic algorithms.
Jörg LässigKarl-Heinz HoffmannMihaela EnachescuPublished in: GECCO (Companion) (2008)
Keyphrases
- genetic algorithm
- probability distribution
- crossover and mutation
- selection strategy
- evolutionary algorithm
- selection algorithm
- crossover operator
- genetic programming
- genetic algorithm ga
- mutation operator
- fitness function
- neural network
- bayesian networks
- random variables
- probability models
- differential evolution
- genetic operators
- evolutionary computation
- optimization method
- probability measure
- hybrid algorithm
- optimization problems
- simulated annealing
- conditional independence
- fuzzy logic
- multi objective
- genetic search
- initial population
- population size
- automatic selection
- parallel genetic algorithm
- function optimization
- penalty function
- classifier systems
- posterior probability
- metaheuristic
- search algorithm