Evolutionary Optimization Algorithms: Fundamentals Training
|Commitment||2 days, 7-8 hours a day.|
|How To Pass||Pass all graded assignments to complete the course.|
|User Ratings||Average User Rating 4.8 See what learners said|
|Delivery Options||Instructor-Led Onsite, Online, and Classroom Live|
Evolutionary Optimization Algorithms: Fundamentals Training Course – Hands-on
Evolutionary Optimization Algorithms: Fundamentals Training Course – Customize it
- We can adapt this training course to your group’s background and work requirements at little to no added cost.
- If you are familiar with some aspects of this training course, we can omit or shorten their discussion.
- We can adjust the emphasis placed on the various topics or build the training around the mix of technologies of interest to you (including technologies other than those included in this outline).
- If your background is nontechnical, we can exclude the more technical topics, include the topics that may be of special interest to you (e.g., as a manager or policy-maker), and present the training course in manner understandable to lay audiences.
Evolutionary Optimization Algorithms: Fundamentals Training Course – Audience/Target Group
The target audience for this training course:
Evolutionary Optimization Algorithms: Fundamentals Training Course – Objectives:
Upon completing this training course, learners will be able to meet these objectives:
- 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.
Evolutionary Optimization Algorithms: Fundamentals Training – Course Content
Evolutionary Optimization Algorithms: Fundamentals Training – Introduction. Terminology. Unconstrained optimization. Constrained optimization. Multi-objective optimization. Multimodal optimization. Combinatorial optimization. Hill climbing algorithms.
Evolutionary Optimization Algorithms: Fundamentals Training – 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.