Essentials of Metaheuristics (Second Edition)

Interested in the Genetic Algorithm? Simulated Annealing? Ant Colony Optimization? Essentials of Metaheuristics covers these and other metaheuristics algorithms and is intended for undergraduate students programmers and non-experts. The book covers a wide range of algorithms representations selection and modification operators and related topics and includes 71 figures and 135 algorithms great and small. Algorithms include: Gradient Ascent techniques Hill-Climbing variants Simulated Annealing Tabu Search variants Iterated Local Search Evolution Strategies the Genetic Algorithm the Steady-State Genetic Algorithm Differential Evolution Particle Swarm Optimization Genetic Programming variants One- and Two-Population Competitive Coevolution N-Population Cooperative Coevolution Implicit Fitness Sharing Deterministic Crowding NSGA-II SPEA2 GRASP Ant Colony Optimization variants Guided Local Search LEM PBIL UMDA cGA BOA SAMUEL ZCS XCS and XCSF.