%0 Journal Article %1 Wang:2006:GPEM %A Wang, Yao %A Wineberg, Mark %D 2006 %J Genetic Programming and Evolvable Machines %K Dynamic Evolvability, Price's algorithms, environment, equation genetic %N 4 %P 355--382 %R doi:10.1007/s10710-006-9015-5 %T Estimation of evolvability genetic algorithm and dynamic environments %V 7 %X This article investigates the of applicability of adding evolvability promoting mechanisms to a genetic algorithm to enhance its ability to handle perpetually novel dynamic environments, especially one that has stationary periods allowing the Genetic Algorithm (GA) to converge on a temporary global optimum.We use both biological and evolutionary computation (EC) definitions of evolvability to create two measures: one based on the improvements in fitness; the other based on the amount of genotypic change. These two evolvability measures are used alongside fitness to modify how selection proceeds in the GA. We call this modified GA the Estimation of Evolvability Genetic Algorithm (EEGA). When tested against a regular GA (with random immigrants), the EEGA is able to track the global optimum more closely than the GA dug the dynamic period. Unlike most GA extensions, the EEGA works effectively at a lower level of diversity than does the GA, showing that it is the quality of the diverse members in the population and not just the quantity that helps the GA evolve.

@article{Wang:2006:GPEM, abstract = {This article investigates the of applicability of adding evolvability promoting mechanisms to a genetic algorithm to enhance its ability to handle perpetually novel dynamic environments, especially one that has stationary periods allowing the Genetic Algorithm (GA) to converge on a temporary global optimum.We use both biological and evolutionary computation (EC) definitions of evolvability to create two measures: one based on the improvements in fitness; the other based on the amount of genotypic change. These two evolvability measures are used alongside fitness to modify how selection proceeds in the GA. We call this modified GA the Estimation of Evolvability Genetic Algorithm (EEGA). When tested against a regular GA (with random immigrants), the EEGA is able to track the global optimum more closely than the GA dug the dynamic period. Unlike most GA extensions, the EEGA works effectively at a lower level of diversity than does the GA, showing that it is the quality of the diverse members in the population and not just the quantity that helps the GA evolve.}, added-at = {2008-06-19T17:35:00.000+0200}, author = {Wang, Yao and Wineberg, Mark}, biburl = {https://www.bibsonomy.org/bibtex/247a1a437b16d6bb604db100fe3fc2d62/brazovayeye}, doi = {doi:10.1007/s10710-006-9015-5}, interhash = {ea1c1e1cbda8d4f1a20903690c884e61}, intrahash = {47a1a437b16d6bb604db100fe3fc2d62}, issn = {1389-2576}, journal = {Genetic Programming and Evolvable Machines}, keywords = {Dynamic Evolvability, Price's algorithms, environment, equation genetic}, month = {December}, notes = {p357 'evolution tends to retain solutions that have a more evolvable genetic system' VEGA like. 3 dynamic selection pressures. Efficient diversity measures. F8F2. Binary graycode.}, number = 4, pages = {355--382}, size = {28 pages}, timestamp = {2008-06-19T17:53:52.000+0200}, title = {Estimation of evolvability genetic algorithm and dynamic environments}, volume = 7, year = 2006 }