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

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pApplication Areas of Evolutionary Algorithms EAs are popular for their simplicity, effectiveness, robustness. Holland: ”It’s best used in areas where you don’t really have a good idea what the solution might be. And it often surprises you with what you come up with.” Applications control, engineering design, image processing, planning & scheduling, VLSI circuit design, network optimization & routing problems, optimal resource allocation, marketing, credit scoring & risk assessment, and many others. Evolutionary Algorithms: Intro
pMultiple Traveling Salesmen Problem Rescue operations planning Given N cities and K agents, find an optimal tour for each agent so that every city is visited exactly once. A typical criterion to be optimized is the overall time spent by the squad (i.e., the slowest team member) during the task execution. 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: pReplacement Strategy Replacement s
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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