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

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pRepresentation :: Problem can be represented as binary string – real-valued string – string of chars – or as a tree or as a graph, and others. Evolutionary Algorithms: Intro
pEvaluation Function :: Objective (Fitness) function the only information about the sought solution the algorithm dispose of, must be defined for every possible chromosome. :: Fitness function may be multimodal, discrete, multidimensional, nonlinear, noisy, :: Fitness does not have to be define analytically simulation results, classification success rate. :: Fitness function should not be too costly!!! multiobjective. Evolutionary Algorithms: Intro
Page 1 and 2: Evolutionary Algorithms: Introducti
Page 3 and 4: 4. Real-coded EAs Evolution strate
Page 5 and 6: pEvolution Strategies: Optimization
Page 7: pEA: Vocabulary :: Vocabulary borro
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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