Abstract
Evolutionary Programming (EP) belongs to a class of general optimization algorithms based on the model of natural evolution. EP has also been applied to real-valued function optimization since the early 90's. However, recent research results have proved that EP is not so robust as expected; EP performs very well only when the lower bound of strategy parameters is adjusted to each problem. In order to overcome this difficulty, an extended EP, called Robust EP (REP), is proposed. A major feature of REP is that genetic drift is introduced as another source of changing strategy parameters. Computer simulations are conducted in order to illustrate the robust performance of REP against the lower bound on a set of popular benchmark problems. Some evolutionary characteristics of REP are also clarified by calculating basic statistical values.
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