GA performance in a babel-like fitness landscape

Hideaki Suzuki, Yoh Iwasa

Research output: Contribution to journalConference articlepeer-review

3 Citations (Scopus)


The performance of genetic algorithms (GAs) is studied under a babel-like fitness landscape, in which only a one bit sequence is significantly advantageous over the others. Under this landscape, the most dominant process to determine the GA performance is creation of the advantageous sequence, and crossover facilitates the creation, thereby improves the GA performance. We first conduct a computer simulation using the simple GA, and examine the waiting time until domination of the advantageous sequence (Td). It is shown that crossover with mildly high rate reduces Td significantly and that the magnitude of this reduction (Across) is the largest when the mutation rate is an intermediate value. Second, we mathematically analyze the model and estimate the value of Across. From these observations, we determine implementation criteria for GAs, which are useful when we apply GAs to engineering problems such as having a conspicuously discontinuous fitness landscape.

Original languageEnglish
Pages (from-to)357-366
Number of pages10
JournalProceedings of the International Conference on Tools with Artificial Intelligence
Publication statusPublished - 1997
EventProceedings if the 1997 IEEE 9th IEEE International Conference on Tools with Artificial Intelligence - Newport Beach, CA, USA
Duration: Nov 3 1997Nov 8 1997

All Science Journal Classification (ASJC) codes

  • Software


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