Reduction of fitness evaluations using clustering techniques and neural network ensembles
First Claim
1. An evolutionary optimization method comprising the steps of:
- (a) setting up an initial population of individuals and applying an original fitness function;
(b) selecting offspring individuals having a high evaluated quality value as parents;
(c) reproducing the parents to create a plurality of offspring individuals;
(d) evaluating the quality of the plurality of offspring individuals by means of a fitness function, wherein selectively the original fitness function or an approximate fitness function is used, including the steps of;
grouping all λ
of the plurality of offspring individuals into clusters;
selecting for each cluster one or more offspring individuals, resulting in altogether ξ
selected offspring individuals;
evaluating the ξ
selected offspring individuals by means of the original fitness function; and
evaluating the remaining λ
-ξ
offspring individuals by means of the approximate fitness function; and
(e) repeating steps (b) through (d) until a termination condition is met.
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Abstract
One embodiment of the invention proposes an evolutionary optimization method. In a first step, an initial population of individuals is set up and an original fitness function is applied. Then the offspring individuals having a high evaluated quality value as parents are selected. In a third step, the parents are reproduced to create a plurality of offspring individuals. The quality of the offspring individuals is evaluated by means of a fitness function, wherein selectively the original or an approximate fitness function is used. Finally, the method goes back to the selection step until a termination condition is met. According to an embodiment, the step of evaluating the quality of the offspring individuals consists in grouping all λ offspring individuals in clusters, selecting for each cluster one or a plurality of offspring individuals, resulting in altogether ξ selected offspring individuals, evaluating the ξ selected offspring individuals by means of the original fitness function, and evaluating the remaining λ-ξ offspring individuals by means of the approximate fitness function.
65 Citations
12 Claims
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1. An evolutionary optimization method comprising the steps of:
-
(a) setting up an initial population of individuals and applying an original fitness function;
(b) selecting offspring individuals having a high evaluated quality value as parents;
(c) reproducing the parents to create a plurality of offspring individuals;
(d) evaluating the quality of the plurality of offspring individuals by means of a fitness function, wherein selectively the original fitness function or an approximate fitness function is used, including the steps of;
grouping all λ
of the plurality of offspring individuals into clusters;
selecting for each cluster one or more offspring individuals, resulting in altogether ξ
selected offspring individuals;
evaluating the ξ
selected offspring individuals by means of the original fitness function; and
evaluating the remaining λ
-ξ
offspring individuals by means of the approximate fitness function; and
(e) repeating steps (b) through (d) until a termination condition is met. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)
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Specification