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

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pArtificial Ant Problem: Example cont. Ant behavior What happens if the ant ”hits” an obstacle? What is strange with transition from state 10 to the initial state 00? When does the ant succeed? Is the number of states sufficient to solve the problem? Do all of the possible 34-bit chromosomes represent a feasible solution? Evolutionary Algorithms: Intro
pArtificial Ant Problem: GA result Representation 32 states, 453 = 64 × 7 + 5 bits !!! Population size: 65.536 !!! Number of generations: 200 Total number of samples tried: 13 × 10 6 !!! 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 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: pArtificial Ant Problem: GA Approac
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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