A novel pseudoderivative-based mutation operator for real-coded adaptive genetic algorithms.

ABSTRACT: Recent development of large databases, especially those in genetics and proteomics, is pushing the development of novel computational algorithms that implement rapid and accurate search strategies. One successful approach has been to use artificial intelligence and methods, including pattern recognition (e.g. neural networks) and optimization techniques (e.g. genetic algorithms). The focus of this paper is on optimizing the design of genetic algorithms by using an adaptive mutation rate that is derived from comparing the fitness values of successive generations. We propose a novel pseudoderivative-based mutation rate operator designed to allow a genetic algorithm to escape local optima and successfully continue to the global optimum. Once proven successful, this algorithm can be implemente

SUBMITTER: Kanwal MS 

PROVIDER: S-EPMC3924953 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature