Lamarckian evolution, the Baldwin effect and function optimization
We compare two forms of hybrid genetic search. The first uses Lamarckian evolution, while the second uses a related method where local search is employed to change the fitness of strings, but the acquired improvements do not change the genetic encoding of the individual. The latter search method exploits the Baldwin effect. By modeling a simple genetic algorithm we show that functions exist where simple genetic algorithms without learning as well as Lamarckian evolution converge to the same local optimum, while genetic search utilizing the Baldwin effect converges to the global optimum. We also show that a simple genetic algorithm exploiting the Baldwin effect can sometimes outperform forms of Lamarckian evolution that employ the same local search strategy.
KeywordsGenetic Algorithm Local Search Saddle Point Local Optimum Fitness Landscape
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