Abstract
We propose an algorithm for multi-objective
optimization using a mixture-based iterated
density estimation evolutionary algorithm
(MIDEA). The MIDEA algorithm is a prob-
abilistic model building evolutionary algo-
rithm that constructs at each generation a
mixture of factorized probability distribu-
tions. The use of a mixture distribution gives
us a powerful, yet computationally tractable,
representation of complicated dependencies.
In addition it
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