Evolutionary design of Evolutionary Algorithms Export

@article{citeulike:4205331, abstract = {Abstract\ \ Manual design of Evolutionary Algorithms (EAs) capable of performing very well on a wide range of problems is a difficult task. This is why we have to find other manners to construct algorithms that perform very well on some problems. One possibility (which is explored in this paper) is to let the evolution discover the optimal structure and parameters of the EA used for solving a specific problem. To this end a new model for automatic generation of EAs by evolutionary means is proposed here. The model is based on a simple Genetic Algorithm (GA). Every GA chromosome encodes an EA, which is used for solving a particular problem. Several Evolutionary Algorithms for function optimization are generated by using the considered model. Numerical experiments show that the EAs perform similarly and sometimes even better than standard approaches for several well-known benchmarking problems.}, author = {Dio\c{s}an, Laura and Oltean, Mihai}, citeulike-article-id = {4205331}, citeulike-linkout-0 = {http://dx.doi.org/10.1007/s10710-009-9081-6}, citeulike-linkout-1 = {http://www.springerlink.com/content/e1614637t3360717}, day = {1}, doi = {10.1007/s10710-009-9081-6}, journal = {Genetic Programming and Evolvable Machines}, keywords = {continuous, evolutionary-computation, genetic-algorithm, genetic-programming, metaheuristic, parameter-setting, performance-assessment}, month = {September}, number = {3}, pages = {263--306}, posted-at = {2009-03-23 10:24:43}, priority = {5}, title = {Evolutionary design of Evolutionary Algorithms}, url = {http://dx.doi.org/10.1007/s10710-009-9081-6}, volume = {10}, year = {2009} }

TY - JOUR ID - citeulike:4205331 L3 - citeulike-article-id:4205331 N2 - Abstract  Manual design of Evolutionary Algorithms (EAs) capable of performing very well on a wide range of problems is a difficult task. This is why we have to find other manners to construct algorithms that perform very well on some problems. One possibility (which is explored in this paper) is to let the evolution discover the optimal structure and parameters of the EA used for solving a specific problem. To this end a new model for automatic generation of EAs by evolutionary means is proposed here. The model is based on a simple Genetic Algorithm (GA). Every GA chromosome encodes an EA, which is used for solving a particular problem. Several Evolutionary Algorithms for function optimization are generated by using the considered model. Numerical experiments show that the EAs perform similarly and sometimes even better than standard approaches for several well-known benchmarking problems. IS - 3 JF - Genetic Programming and Evolvable Machines EP - 306 TI - Evolutionary design of Evolutionary Algorithms VL - 10 SP - 263 KW - continuous KW - evolutionary-computation KW - genetic-algorithm KW - genetic-programming KW - metaheuristic KW - parameter-setting KW - performance-assessment AU - Dioşan, Laura AU - Oltean, Mihai PY - 2009/09/01/ UR - http://dx.doi.org/10.1007/s10710-009-9081-6 ER -