CS 478 - Evolutionary Algorithms 1 - Neural Networks and Machine ...
CS 478 - Evolutionary Algorithms 1 - Neural Networks and Machine ... CS 478 - Evolutionary Algorithms 1 - Neural Networks and Machine ...
Similar to Evolutionary Programming - initially used for optimization of complex real systems - fluid dynamics, etc. - usually real vectors Uses both crossover and mutation. Crossover first. Also averaging crossover (real values), and multi-parent. Randomly selects set of parents and modifies them to create > n children Two survival schemes – Keep best n of combined parents and children – Keep best n of only children CS 478 - Evolutionary Algorithms 20
Evolves more complex structures - programs, Lisp code, neural networks Start with random programs of functions and terminals (data structures) Execute programs and give each a fitness measure Use crossover to create new programs, no mutation Keep best programs For example, place lisp code in a tree structure, functions at internal nodes, terminals at leaves, and do crossover at sub-trees - always legal in Lisp CS 478 - Evolutionary Algorithms 21
- Page 1 and 2: CS 478 - Evolutionary Algorithms 1
- Page 3 and 4: Populate our search space with init
- Page 5 and 6: Procedure EA t = 0; Initialize Popu
- Page 7 and 8: CS 478 - Evolutionary Algorithms 7
- Page 9 and 10: Individuals are represented so that
- Page 11 and 12: CS 478 - Evolutionary Algorithms 11
- Page 13 and 14: In general want the fittest parents
- Page 15 and 16: Population size - Larger gives more
- Page 17 and 18: There exist mathematical proofs tha
- Page 19: Representation that best fits probl
- Page 23 and 24: Use a Genetic Algorithm to learn th
- Page 25 and 26: Much current work and extensions Nu
- Page 27 and 28: Each classifier has an associated s
- Page 29: s(C,t+1) = s(C,t) -B(C,t) - Loss of
Similar to <strong>Evolutionary</strong> Programming - initially used for<br />
optimization of complex real systems - fluid dynamics, etc.<br />
- usually real vectors<br />
Uses both crossover <strong>and</strong> mutation. Crossover first. Also<br />
averaging crossover (real values), <strong>and</strong> multi-parent.<br />
R<strong>and</strong>omly selects set of parents <strong>and</strong> modifies them to<br />
create > n children<br />
Two survival schemes<br />
– Keep best n of combined parents <strong>and</strong> children<br />
– Keep best n of only children<br />
<strong>CS</strong> <strong>478</strong> - <strong>Evolutionary</strong> <strong>Algorithms</strong> 20
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