Improved evolutionary optimization from genetically adaptive multimethod search.

ABSTRACT: In the last few decades, evolutionary algorithms have emerged as a revolutionary approach for solving search and optimization problems involving multiple conflicting objectives. Beyond their ability to search intractably large spaces for multiple solutions, these algorithms are able to maintain a diverse population of solutions and exploit similarities of solutions by recombination. However, existing theory and numerical experiments have demonstrated that it is impossible to develop a single algorithm for population evolution that is always efficient for a diverse set of optimization problems. Here we show that significant improvements in the efficiency of evolutionary search can be achieved by running multiple optimization algorithms simultaneously using new concepts of global information

SUBMITTER: Vrugt JA 

PROVIDER: S-EPMC1766340 | biostudies-literature | 2007 Jan

REPOSITORIES: biostudies-literature