Genetic Algorithms
Advanced programming[1]
Start here to understand genetic algorithms
Brute force approach Combinatorial optimization Computational intractability Convergence Crossover / crossover operator Elitism Exploration vs exploitation Fitness / fitness function / fitness landscape Heuristic Hill climbing [[Initialization parameters Local extrema Mating pool Mutation / mutation rate Novelty search Offspring Optimization Population Premature convergence Problem space Ranking Roulette wheel selection Selection strategy Simulated annealing Stochastic universal sampling Termination condition Tour Tournament selection Truncation selection