Decision making in a hybrid genetic algorithm
FG Lobo, DE Goldberg - Proceedings of 1997 IEEE …, 1997 - ieeexplore.ieee.org
Proceedings of 1997 IEEE International Conference on Evolutionary …, 1997•ieeexplore.ieee.org
There are several issues that need to be taken into consideration when designing a hybrid
problem solver. The paper focuses on one of them-decision making. More specifically, we
address the following questions: given two different methods, how to get the most out of both
of them? When should we use one and when should we use the other in order to get
maximum efficiency? We present a model for hybridizing genetic algorithms (GAs) based on
a concept that decision theorists call probability matching and we use it to combine an elitist …
problem solver. The paper focuses on one of them-decision making. More specifically, we
address the following questions: given two different methods, how to get the most out of both
of them? When should we use one and when should we use the other in order to get
maximum efficiency? We present a model for hybridizing genetic algorithms (GAs) based on
a concept that decision theorists call probability matching and we use it to combine an elitist …
There are several issues that need to be taken into consideration when designing a hybrid problem solver. The paper focuses on one of them-decision making. More specifically, we address the following questions: given two different methods, how to get the most out of both of them? When should we use one and when should we use the other in order to get maximum efficiency? We present a model for hybridizing genetic algorithms (GAs) based on a concept that decision theorists call probability matching and we use it to combine an elitist selecto-recombinative GA with a simple hill climber (HC). Tests on an easy problem with a small population size match our intuition that both GA and HC are needed to solve the problem efficiently.
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