About the Book
Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. Pages: 90. Chapters: Newton's method, Genetic algorithm, Greedy algorithm, Dynamic programming, Minimax, Alpha-beta pruning, Random optimization, Simulated annealing, CMA-ES, Simplex algorithm, Swarm intelligence, Particle swarm optimization, Criss-cross algorithm, Imperialist competitive algorithm, Divide and conquer algorithm, Harmony search, Bees algorithm, Differential evolution, Matrix chain multiplication, Bin packing problem, Evolutionary algorithm, Nelder-Mead method, Extremal optimization, Hill climbing, IOSO, Reactive search optimization, Cutting-plane method, Guided Local Search, Automatic label placement, Karmarkar's algorithm, Cuckoo search, Evolutionary multi-modal optimization, Job shop scheduling, Cross-entropy method, Meta-optimization, Interior point method, Crew scheduling, Auction algorithm, Artificial Bee Colony Algorithm, Tabu search, Augmented Lagrangian method, Firefly algorithm, BRST algorithm, Quantum annealing, Pattern search, Graduated optimization, Branch and bound, Fourier-Motzkin elimination, Random search, Bland's rule, Maximum subarray problem, Negamax, Genetic algorithms in economics, Tree rearrangement, Glowworm swarm optimization, Sequential minimal optimization, Branch and cut, Delayed column generation, Very large-scale neighborhood search, Mehrotra predictor-corrector method, Penalty method, BHHH algorithm, Evolutionary programming, Destination dispatch, Great Deluge algorithm, Iterated local search, Big M method, Lemke's algorithm, Sequence-dependent setup, Ordered subset expectation maximization, MCS algorithm, Zionts-Wallenius method, Biologically inspired algorithms, Rosenbrock methods, Stochastic hill climbing, Optimization algorithm. Excerpt: A genetic algorithm (GA) is a search heuristic that mimics the process of natural evolution. This heuristic is routinely used to generate useful...
