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A B C D E F G H I J K L M N O P Q R S T U V W X Y Z
SEE THE SOURCES USED FOR THE GLOSSARY DEFINITIONS HERE
GENERATED NODE
A node created as a result of node expansion.
See Also:  Expanded Node, Node
Applications:  desktopUSS
GENERATION
An iteration of an evolutionary algorithm.
See Also:  Evolutionary Algorithm
Applications:  desktopGA
GENETIC ALGORITHM
Genetic algorithms are evolutionary algorithms that represent individuals as fixed-length character strings (chromosomes made of units known as genes). Classic genetic algorithms use binary strings but integer vectors are commonly used as well.

Three types of operators are generally involved: selection, crossover (single point), and mutation. The basic idea is that children inherit gene values from more than one parent. This mixing of parental gene values, along with an occasional mutation, provides the potential for a much more aggressive exploration of the search space.

See Also:  Crossover, Evolutionary Algorithm, Mutation, Selection
Applications:  desktopGA
GOAL STATE
Goal-based agents act to achieve a goal (a set of environment states). The agent’s task is to find out how to act so that it reaches a goal state.
 
Goal State
Travel Through Switzerland – Goal State
See Also:  Search, State
Applications:  desktopUSS