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

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pIdealized Illustration of Evolution Uniform sampled population. Population converged to promising regions. Evolutionary Algorithms: Intro
pInitialization :: Random randomly generated solutions, no prior information about the shape of the sought solution, relies just on ”lucky” sampling of the whole search space by a finite set of samples. :: Informed (pre-processing) (meta)heuristic routines used for seeding the initial population, biased random generator sampling regions of the search space that are likely to contain the sought solutions, + may help to find better solutions, + may speed up the search process, – may cause irreversible focusing of the search process on regions with local optima. 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 and 8: pEA: Vocabulary :: Vocabulary borro
Page 9 and 10: pEvaluation Function :: Objective (
Page 11: pEvolutionary Cycle
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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