Genetic Algorithms Chapter 9, Complexity: A Guided Tour.

Download Genetic Algorithms Chapter 9, Complexity: A Guided Tour.

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Genetic Algorithms and Nano-computing0123456789 0 1 2 3 4 5 6 7 8 9Time: 0Score: 00123456789 0 1 2 3 4 5 6 7 8 9Time: 1Score: 00123456789 0 1 2 3 4 5 6 7 8 9Time: 2Score: 50123456789 0 1 2 3 4 5 6 7 8 9Time: 2Score: 5Evolution by Natural SelectionCharles DarwinOrganisms inherit traits from parentsTraits are inherited with some variation, via mutation and sexual recombinationDue to competition for limited resources, the organisms best adapted to the environment tend to produce the most offspring. Evolution by Natural SelectionCharles DarwinOrganisms inherit traits from parentsTraits are inherited with some variation, via mutation and sexual recombinationDue to competition for limited resources, the organisms best adapted to the environment tend to produce the most offspring. This way traits producing adapted individuals spread in the populationEvolution by Natural SelectionOrganisms inherit traits from parentsTraits are inherited with some variation, via mutation and sexual recombinationDue to competition for limited resources, the organisms best adapted to the environment tend to produce the most offspring. This way traits producing adapted individuals spread in the populationin computersComputer(e.g., programs)Charles DarwinEvolution by Natural SelectionOrganisms inherit traits from parentsTraits are inherited with some variation, via mutation and sexual recombinationDue to competition for limited resources, the organisms best adapted to the environment tend to produce the most offspring. This way traits producing adapted individuals spread in the populationin computersComputer(e.g., programs)Charles DarwinJohn Holland0123456789 0 1 2 3 4 5 6 7 8 9Time: 0Score: 00123456789 0 1 2 3 4 5 6 7 8 9Time: 1Score: 00123456789 0 1 2 3 4 5 6 7 8 9Time: 2Score: 00123456789 0 1 2 3 4 5 6 7 8 9Time: 3Score: 00123456789 0 1 2 3 4 5 6 7 8 9Time: 0Score: 00123456789 0 1 2 3 4 5 6 7 8 9Time: 1Score: 00123456789 0 1 2 3 4 5 6 7 8 9Time: 2Score: 00123456789 0 1 2 3 4 5 6 7 8 9Time: 3Score: 100123456789 0 1 2 3 4 5 6 7 8 9Time: 4Score: 100123456789 0 1 2 3 4 5 6 7 8 9Time: 5Score: 200123456789 0 1 2 3 4 5 6 7 8 9Time: 6Score: 200123456789 0 1 2 3 4 5 6 7 8 9Time: 7Score: 200123456789 0 1 2 3 4 5 6 7 8 9Time: 8Score: 200123456789 0 1 2 3 4 5 6 7 8 9Time: 9Score: 200123456789 0 1 2 3 4 5 6 7 8 9Time: 10Score: 200123456789 0 1 2 3 4 5 6 7 8 9Time: 11Score: 200123456789 0 1 2 3 4 5 6 7 8 9Time: 12Score: 200123456789 0 1 2 3 4 5 6 7 8 9Time: 13Score: 200123456789 0 1 2 3 4 5 6 7 8 9Time: 14Score: 300123456789 0 1 2 3 4 5 6 7 8 9Time: 15Score: 300123456789 0 1 2 3 4 5 6 7 8 9Time: 16Score: 400123456789 0 1 2 3 4 5 6 7 8 9Time: 17Score: 400123456789 0 1 2 3 4 5 6 7 8 9Time: 18Score: 500123456789 0 1 2 3 4 5 6 7 8 9Time: 19Score: 500123456789 0 1 2 3 4 5 6 7 8 9Time: 20Score: 600123456789 0 1 2 3 4 5 6 7 8 9Time: 0Score: 00123456789 0 1 2 3 4 5 6 7 8 9Time: 1Score: 00123456789 0 1 2 3 4 5 6 7 8 9Time: 2Score: 100123456789 0 1 2 3 4 5 6 7 8 9Time: 3Score: 100123456789 0 1 2 3 4 5 6 7 8 9Time: 4Score: 200123456789 0 1 2 3 4 5 6 7 8 9Time: 5Score: 200123456789 0 1 2 3 4 5 6 7 8 9Time: 6Score: 200123456789 0 1 2 3 4 5 6 7 8 9Time: 7Score: 200123456789 0 1 2 3 4 5 6 7 8 9Time: 8Score: 200123456789 0 1 2 3 4 5 6 7 8 9Time: 9Score: 300123456789 0 1 2 3 4 5 6 7 8 9Time: 10Score: 300123456789 0 1 2 3 4 5 6 7 8 9Time: 11Score: 400123456789 0 1 2 3 4 5 6 7 8 9Time: 12Score: 400123456789 0 1 2 3 4 5 6 7 8 9Time: 13Score: 500123456789 0 1 2 3 4 5 6 7 8 9Time: 14Score: 500123456789 0 1 2 3 4 5 6 7 8 9Time: 15Score: 600123456789 0 1 2 3 4 5 6 7 8 9Time: 16Score: 60Generation 10Best average score = 0Generation 200Fitness = 240Generation 1000Fitness = 4920123456789 0 1 2 3 4 5 6 7 8 9Time: 17Score: 700123456789 0 1 2 3 4 5 6 7 8 9Time: 18Score: 70Why Did The GAs Strategy Outperform Mine?My Strategy0123456789 0 1 2 3 4 5 6 7 8 90123456789 0 1 2 3 4 5 6 7 8 90123456789 0 1 2 3 4 5 6 7 8 90123456789 0 1 2 3 4 5 6 7 8 90123456789 0 1 2 3 4 5 6 7 8 9The GAs Evolved Strategy0123456789 0 1 2 3 4 5 6 7 8 90123456789 0 1 2 3 4 5 6 7 8 90123456789 0 1 2 3 4 5 6 7 8 90123456789 0 1 2 3 4 5 6 7 8 90123456789 0 1 2 3 4 5 6 7 8 90123456789 0 1 2 3 4 5 6 7 8 90123456789 0 1 2 3 4 5 6 7 8 90123456789 0 1 2 3 4 5 6 7 8 90123456789 0 1 2 3 4 5 6 7 8 90123456789 0 1 2 3 4 5 6 7 8 90123456789 0 1 2 3 4 5 6 7 8 9Principles of Evolution Seen in Genetic AlgorithmsPrinciples of Evolution Seen in Genetic AlgorithmsNatural selection works!Principles of Evolution Seen in Genetic AlgorithmsNatural selection works!Evolution proceeds via periods of stasis punctuated by periods of rapid innovationPrinciples of Evolution Seen in Genetic AlgorithmsNatural selection works!Evolution proceeds via periods of stasis punctuated by periods of rapid innovationBest fitness in populationGeneration numberPrinciples of Evolution Seen in Genetic AlgorithmsNatural selection works!Evolution proceeds via periods of stasis punctuated by periods of rapid innovationExaptation is commonPrinciples of Evolution Seen in Genetic AlgorithmsNatural selection works!Evolution proceeds via periods of stasis punctuated by periods of rapid innovationExaptation is commonCo-evolution speeds up innovationPrinciples of Evolution Seen in Genetic AlgorithmsNatural selection works!Evolution proceeds via periods of stasis punctuated by periods of rapid innovationExaptation is commonCo-evolution speeds up innovationDynamics and results of evolution are unpredictable and hard to analyze

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