Improved evolutionary optimization from genetically adaptive multimethod search

@article{citeulike:1113804, 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 sharing and genetically adaptive offspring creation. We call this approach a multialgorithm, genetically adaptive multiobjective, or AMALGAM, method, to evoke the image of a procedure that merges the strengths of different optimization algorithms. Benchmark results using a set of well known multiobjective test problems show that AMALGAM approaches a factor of 10 improvement over current optimization algorithms for the more complex, higher dimensional problems. The AMALGAM method provides new opportunities for solving previously intractable optimization problems. 10.1073/pnas.0610471104}, author = {Vrugt, Jasper A. and Robinson, Bruce A. }, citeulike-article-id = {1113804}, journal = {PNAS}, keywords = {jr20070305}, month = {January}, number = {3}, pages = {708--711}, posted-at = {2007-02-20 05:19:50}, priority = {2}, title = {Improved evolutionary optimization from genetically adaptive multimethod search}, url = {http://www.pnas.org/cgi/content/abstract/104/3/708}, volume = {104}, year = {2007} }

TY - JOUR ID - citeulike:1113804 L3 - citeulike-article-id:1113804 TI - Improved evolutionary optimization from genetically adaptive multimethod search JF - PNAS VL - 104 IS - 3 SP - 708 EP - 711 N2 - 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 sharing and genetically adaptive offspring creation. We call this approach a multialgorithm, genetically adaptive multiobjective, or AMALGAM, method, to evoke the image of a procedure that merges the strengths of different optimization algorithms. Benchmark results using a set of well known multiobjective test problems show that AMALGAM approaches a factor of 10 improvement over current optimization algorithms for the more complex, higher dimensional problems. The AMALGAM method provides new opportunities for solving previously intractable optimization problems. 10.1073/pnas.0610471104 KW - jr20070305 AU - Vrugt, Jasper AU - Robinson, Bruce PY - 2007/01/16/ UR - http://www.pnas.org/cgi/content/abstract/104/3/708 ER -