Evolutionary Algorithms: Introduction Jiˇr´ı Kubal´ık Department of ...

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pContents 1. Introduction to Evolutionary Algorithms (EAs) Pioneers of EAs, Simple Genetic Algorithm (SGA), Areas for EA’s applications, SGA example: Evolving strategy for an artificial ant problem. Schema theory – a schema, its properties, exponential growth equation and its consequences. 2. Genetic Programming (GP) and Grammatical Evolution (GE) Tree representation, closure condition, ’strong typing’, Application of GP to the artificial ant problem, Other examples. 3. Multi-objective EAs (MOEAs) Multi-objective optimization Concept of dominance and Pareto-optimality, NSGA, NSGA-II, SPEA, SPEA2. Evolutionary Algorithms: Intro
4. Real-coded EAs Evolution strategies, Crossover operators for real-coded representation, Differential evolution. 5. EAs for dynamic optimization problems 6. Swarm Intelligence Ant Colony Optimization, Particle Swarm Optimization. Evolutionary Algorithms: Intro
Page 1: Evolutionary Algorithms: Introducti
Page 5 and 6: pEvolution Strategies: Optimization
Page 7 and 8: pEA: Vocabulary :: Vocabulary borro
Page 9 and 10: pEvaluation Function :: Objective (
Page 11 and 12: pEvolutionary Cycle
Page 13 and 14: pInitialization :: Random randomly
Page 15 and 16: pReproduction: Premature Convergenc
Page 17 and 18: pStagnation
Page 19 and 20: pScaling :: Linear scaling - adjust
Page 21 and 22: pTournament Selection :: Tournament
Page 23 and 24: pGenetic Operators: Crossover :: Id
Page 25 and 26: pReplacement Strategy Replacement s
Page 27 and 28: pMultiple Traveling Salesmen Proble
Page 29 and 30: pArtificial Ant Problem: GA Approac
Page 31 and 32: pArtificial Ant Problem: GA result
Page 33 and 34: pSchema theory :: Schema - a templa
Page 35 and 36: pSchema Theorem Derivation: Effect
Page 37 and 38: pSchema Theorem Derivation: Effect
Page 39 and 40: pSchema Theorem Derivation (cont.)
Page 41: pEA Materials: Reading, Demos, Soft

Evolutionary Algorithms: Introduction Jiˇr´ı Kubal´ık Department of ...

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