A family of adaptive penalty schemes for steady-state genetic algorithms

Real world engineering optimization problems are often subject to constraints which are complex implicit functions of the design variables. Frequently, such constrained problems are replaced by unconstrained ones by means of penalty functions. A family of adaptive penalty schemes for steady-state ge...

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Bibliographic Details
Published in2012 IEEE Congress on Evolutionary Computation pp. 1 - 8
Main Authors Lemonge, A. C. C., Barbosa, H. J. C., Bernardino, H. S.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2012
Subjects
Benchmark testing
Electronic mail
Evolutionary computation
Genetic algorithms
Optimization
Shafts
Steady-state
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Summary:Real world engineering optimization problems are often subject to constraints which are complex implicit functions of the design variables. Frequently, such constrained problems are replaced by unconstrained ones by means of penalty functions. A family of adaptive penalty schemes for steady-state genetic algorithms is proposed here. For each constraint, a penalty parameter is adaptively computed along the run according to information extracted from the current population, such as the existence of feasible individuals and the level of violation of each constraint. The performance of each variant in the family is examined using test problems from the evolutionary computation as well as mechanical and structural optimization literature.
ISBN:1467315109
9781467315104
ISSN:1089-778X
DOI:10.1109/CEC.2012.6256173