Feasible-Infeasible Two-Population Genetic Algorithm to evolve dungeon levels with dependencies in barrier mechanics.
Breno M. F. VianaLeonardo T. PereiraClaudio Fabiano Motta ToledoSelan R. dos SantosSílvia M. D. M. MaiaPublished in: Appl. Soft Comput. (2022)
Keyphrases
- genetic algorithm
- infeasible solutions
- evolutionary process
- population size
- multi population
- mutation operator
- fitness function
- evolutionary algorithm
- multidimensional knapsack problem
- crossover and mutation
- feasible solution
- binary strings
- evolutionary computation
- evolution strategy
- levels of abstraction
- genetic algorithm ga
- genetic operators
- initial population
- genetic search
- evolution process
- multi objective
- crossover operator
- dependency analysis
- real coded
- genetic programming
- search space
- neural network
- dependency graphs
- penalty function
- hybrid algorithm
- metaheuristic
- fuzzy logic
- constrained optimization
- evolutionary strategy
- data dependencies
- population diversity
- optimization method
- differential evolution
- power system
- particle swarm optimization
- control system
- artificial neural networks
- data sets
- fitness sharing