Evolutionary Optimization Algorithms: Fundamentals Training
Evolutionary Optimization Algorithms: Fundamentals Training Course Description
Evolutionary algorithms (EAs) are approaches to artificial intelligence that are motivated by optimization processes that we observe in nature, such as natural selection, species migration, bird swarms, human culture, and ant colonies. This two-day Evolutionary Optimization Algorithms: Fundamentals Training provides a clear explanation of the basic principles of EAs. The course covers the theory, history, mathematics, and application of EAs to engineering optimization problems. Featured techniques include genetic algorithms, evolutionary programming, evolution strategies, ant colony optimization, particle swarm optimization, differential evolution, biogeography-based optimization, and many others. Matlab-based examples are used during the course to illustrate the algorithms. This application-oriented course helps the student obtain a clear, but theoretically rigorous, understanding of EAs. The course also discusses the similarities and differences between various EAs. This course provides an ideal EA introduction to engineering and computer science professionals.
The difference between evolutionary algorithms (EAs), computer intelligence, population based algorithms, biologically-inspired algorithms, and swarm intelligence.
The four fundamental EAs.
Design and program an EA for my problem.
Some of the important tuning parameters in EAs.
Latest EA techniques.
Similarities and differences between various EA techniques.
The no free lunch theorem and what are its implications for EAs.
Perform a statistically rigorous comparison between the performance of different EAs.
With onsite Training, courses can be scheduled on a date that is convenient for you, and because they can be scheduled at your location, you don’t incur travel costs and students won’t be away from home. Onsite classes can also be tailored to meet your needs. You might shorten a 5-day class into a 3-day class, or combine portions of several related courses into a single course, or have the instructor vary the emphasis of topics depending on your staff’s and site’s requirements.
Introduction. Terminology. Unconstrained optimization. Constrained optimization. Multi-objective optimization. Multimodal optimization. Combinatorial optimization. Hill climbing algorithms.
Genetic Algorithms. History. The binary GA. The continuous GA. Matlab examples.
Performance Testing. Benchmarks. The no free lunch theorem. Overstatements based on simulation results. Random numbers. T tests. F tests.
Evolutionary Programming. Continuous EP. Finite state machines. Discrete EP. The prisoner’s dilemma. The artificial ant problem.
Evolution Strategies. The (1+1)-ES. The 1/5 rule. The (mu+1)-ES. The (mu+lambda)-ES. The (mu,lambda)-ES. Self-adaptive ES.
Evolutionary Algorithm Variations. Initialization. Convergence criteria. Problem representation. Elitism. Steady-state vs. generational EAs. Population diversity. Selection options. Recombination options. Mutation.
Ant Colony Optimization. Pheromone models. The ant system. Continuous optimization. Other ACO models.
Particle Swarm Optimization. The basic PSO algorithm. Velocity limiting. Inertia weighting. Constriction coefficients. Global velocity updates. The fully informed PSO algorithm. Learning from mistakes.
Differential Evolution. The basic DE algorithm. DE variations. Discrete optimization. DE and GAs.
Biogeography-Based Optimization. Biogeography in nature. The basic BBO algorithm. BBO migration curves. Blended migration. BBO variations. BBO and GAs.
Other Evolutionary Algorithms. Genetic programming. Simulated annealing. Estimation of distribution algorithms. Cultural algorithms. Opposition-based learning. Tabu search. The artificial fish swarm algorithm. The group search optimizer. The shuffled frog leaping algorithm. The firefly algorithm. Bacterial foraging optimization. The artificial bee colony algorithm. The gravitational search algorithm. Harmony search. Teaching-learning-based optimization.
Practical Advice. Software bugs. Randomness. The nonlinearity of EA tuning. Information in an EA population. Diversity. Problem-specific information.
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