The Cost of Randomness in Evolutionary Algorithms: Crossover can Save Random Bits.
Carlo KneisslDirk SudholtPublished in: EvoCOP (2023)
Keyphrases
- evolutionary algorithm
- initial population
- evolutionary computation
- differential evolution
- multi objective
- optimization problems
- crossover operator
- genetic programming
- fitness function
- multi objective optimization
- simulated annealing
- genetic algorithm
- mutation operator
- genetic operators
- evolution strategy
- high cost
- differential evolution algorithm
- total cost
- cost sensitive
- mutation rate
- particle swarm
- random number
- fitness landscape
- evolution process
- multidisciplinary design optimization
- pseudo random number generators
- mutation operation
- evolutionary search
- evolutionary strategy
- evolutionary process
- neural network
- uniformly distributed
- artificial neural networks
- search space
- minimum cost
- metaheuristic