Course detail
Applied Evolutionary Algorithms
FIT-EVOAcad. year: 2012/2013
Multiobjective optimization problems, standard approaches and stochastic evolutionary algorithms (EA), simulated annealing (SA). Evolution strategies (ES) and genetic algorithms (GA). Tools for fast prototyping. Representation of problems by graph models. Evolutionary algorithms in engineering applications namely in synthesis and physical design of digital circuits, artificial intelligence, signal processing, scheduling in multiprocessor systems and in business commercial applications.
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Course curriculum
- Evolutionary algorithms, theoretical foundation, basic distribution (GA, EP,GP, ES).
- Genetic algorithms (GA), schemata theory.
- Genetic algorithms using diploids and messy-chromozomes. Specific crossing.
- Repesentative combinatorial optimization problems.
- Evolutionary programming, Hill cimbing algorithm, Simulated annealing.
- Genetic programming.
- Advanced estimation distribution algorithms (EDA).
- Variants of EDA algorithms, UMDA, BMDA and BOA.
- Multimodal and multicriterial optimization.
- Dynamoc optimization problems.
- New evolutionary paradigm: immune systems, differential evolution, SOMA.
- Differential evolution. Particle swarm model.
- Ingeneering tasks and evolutionary algorithms.
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Syllabus
- Evolutionary algorithms, theoretical foundation, basic distribution (GA, EP,GP, ES).
- Genetic algorithms (GA), schemata theory.
- Genetic algorithms using diploids and messy-chromozomes. Specific crossing.
- Repesentative combinatorial optimization problems.
- Evolutionary programming, Hill cimbing algorithm, Simulated annealing.
- Genetic programming.
- Advanced estimation distribution algorithms (EDA).
- Variants of EDA algorithms, UMDA, BMDA and BOA.
- Multimodal and multicriterial optimization.
- Dynamoc optimization problems.
- New evolutionary paradigm: immune systems, differential evolution, SOMA.
- Differential evolution. Particle swarm model.
- Ingeneering tasks and evolutionary algorithms.