# 031207 - Lecture 14 Recall Discussing early work on...

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Lecture 14: 03/12/2007 Recall: Discussing early work on evolutionary computation. L. Fogel in the 1960’s (evolutionary programming) Goal : evolve “machine intelligence” Focus : evolving finite-state machines for sequence prediction Sequence approx. environment Mealy Machine : eg on board last time—given an initial state and a sequence of input values, machine makes state transitions and puts out output values. For sequence prediction: {input values}={output values}={0,1} To “evaluate” a machine wrt sequence prediction / reconstruction, take initial segment of a sequence as input values; see whether ensuing output values match rest of sequence segment. There are many ways to evaluate / quantify the “degree of matching.” Fogel’s approach: have a sequence to “predict.” Start with an essentially random set of (small-ish) Melay machines; evaluate them on first, say, k symbols in sequence; keep best μ of them to be parents of next generation; create next generation as follows: 1. [genetic Г] Mutate all parents; have them on 2*μ machines 2. Evaluate these on sequence segment + next symbol 3. [selection] Retain the best μ as parents for next generation, go back to (1) *How to mutate? Change initial state Eliminate a sate and randomly reassign its incoming arrows Add a state and randomly assign its outgoing arrows (and get into

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• Spring '07
• DELCHAMPS
• Algorithms, Evolution, Randomness, output values, input values, L. Fogel, sequence prediction Sequence

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