E

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
ENVIRONMENT
Agents perceive and act upon the environment they are situated in. Each action results in a new state of the environment.
SEE ALSO:  Action, State
APPLICATIONS:  desktopUSS
EVOLUTIONARY ALGORITHM
Evolutionary algorithms use evolution as an inspiration for solving computational problems. They share the following basic features:

  • A population of individuals which are potential solutions to a given problem,
  • A measure of fitness assigned to individuals that determines the quality of the solution,
  • Selection of individuals based on their fitness to generate new individuals through the use of operators.

Examples include genetic algorithms, genetic programming, and evolution strategies.

STRUCTURE OF AN EVOLUTIONARY ALGORITHM

g ← 0
initialize P(g)
evaluate P(g)
WHILE NOT (termination condition) DO
   g ← g + 1
   select P(g) from P(g-1)
   apply variation operators to P(g)
   evaluate P(g)
END
// P = population, g = generation
EXPANDED NODE
A node is expanded by applying each legal operator to the state the node represents, generating a new set of nodes in the process.
SEE ALSO:  Action, Node, Search Tree, State
APPLICATIONS:  desktopUSS