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

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pArtificial Ant Problem Santa Fe trail 32 × 32 grid with 89 food pieces. Obstacles − 1×, 2× strait, − 1×, 2×, 3× right/left. Ant capabilities detects the food right in front of him in direction he faces. actions observable from outside − MOVE – makes a step and eats a food piece if there is some, − LEFT – turns left, − RIGHT – turns right, − NO-OP – no operation. Goal is to find a strategy that would navigate an ant through the grid so that it finds all the food pieces in the given time (600 time steps). Evolutionary Algorithms: Intro
pArtificial Ant Problem: GA Approach Collins a Jefferson 1991, standard GA using binary representation Representation strategy represented by finite state machine, table of transitions coded as binary chromosomes of fixed length. Example: 4-state FSM, 34-bit long chromosomes (2 + 4 × 8) 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: pMultiple Traveling Salesmen Proble
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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