Genetic programming with multiple initial populations generated by simulated annealing

Mototsuka, Takuya; Hara, Akira; Kushida, Jun-ichi; Takahama, Tetsuyuki

doi:10.1109/iwcia.2013.6624797

Cited by 1 publication

(1 citation statement)

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“…One is the loss of the population diversity. It causes the premature convergence to local optima [2], [3], [4], [5], [9]. Another one is the bloat, which means that the program size of individuals increases exponentially [6], [7] In order to solve these problems, we propose a new method which introduces a concept of genealogy into the population.…”

Section: Introductionmentioning

confidence: 99%

Genetic Programming Using the Best Individuals of Genealogies for Maintaining Population Diversity

Hara

Mototsuka

Kushida

et al. 2015

2015 IEEE International Conference on Systems, Man, and Cybernetics

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Genetic Programming (GP) is an evolutionary optimization method for generating tree structural programs. It is important to maintain the population diversity for preventing GP search from falling into local optima. For this purpose, we propose a new method which introduces a concept of genealogy into the population. We call the method Genetic Programming using the Best Individuals of Genealogies (GPBIG). Information on genealogy is assigned to each individual, and the best-so-far individuals in respective genealogies are preserved as the genealogical elite individuals. The population is reconstituted every generation by selecting the individuals from the pool of the genealogical elite individuals. In addition, the search property shifts from global to local search gradually by extinguishing unnecessary genealogies. We examined the effectiveness of our method by comparing with the standard GP in search performance in three kinds of benchmark problems.

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Section: Introductionmentioning

confidence: 99%