534: Genetic Algorithms
(SO MUCH IOSFEDIJFLO WJ#$*E(SIOF HSODLHWI$#(& EIUS HGDUI UGH.)
|Line 28:||Line 28:|
Revision as of 00:02, 4 January 2013
Title text: Just make sure you don't have it maximize instead of minimize.
Cueball is seen here defining a program (possibly in python). The title is a reference to the type of program Cueball is writing, a Genetic Algorithm.
In the computer science field of artificial intelligence, a genetic algorithm is a search heuristic that mimics the process of natural evolution. This heuristic is routinely used to generate useful solutions to optimization and search problems. Genetic algorithms belong to the larger class of evolutionary algorithms, which generate solutions to optimization problems using techniques inspired by natural evolution, such as inheritance, mutation, selection, and crossover.
The line indicated by an arrow is a reference to the Terminator series, in which the main antagonist is an artificial intelligence known as Skynet.
The line about water crossing is a possible reference to the old computer game Oregon Trail, in which crossing water was hazardous.
- [Code displayed, presumably from an IDE]
- def getSolutionCosts(navigationCode):
- fuelStopCost = 15
- extraComputationCost = 8
- [There is a giant arrow pointing to the next line]
- thisAlgorithmBecomingSkynetCost = 999999999
- waterCrossingCost = 45
- Narration: Genetic algorithms tip: *Always* include this in your fitness function.