×

GATE

swMATH ID: 6442
Software Authors: Dominique, Stéphane Author Profile; Trépanier, Jean-Yves; Tribes, Christophe
Description: GATE: A genetic algorithm designed for expensive cost functions. We introduce the GATE algorithm, which was specifically designed to lessen the cost of genetic algorithms (GAs) for engineering design problems. The main strength of the algorithm is to find a good design using a relatively low number of function evaluations. The heart of the algorithm is a new heuristic called territorial core evolution (TE). TE regulates the mean step and the permitted search area of the GAs’ random search operators, depending on the state of convergence of the algorithm. As a result, more global or more local searches are made when necessary to better fit the specificities of each problem. GATE, which was initially calibrated using a Gaussian landscape generator as test case, is shown to be very efficient to solve that kind of topology, especially for large scale problems. Application of the GATE algorithm to various classical test cases allows a better understanding of the strengths and limitations of this algorithm.
Homepage: http://inderscience.metapress.com/content/8474276806547j13/
Keywords: optimisation; genetic algorithms; territorial core evolution; large scale design problems; academic test cases; engineering design; numerical examples; GATE algorithm; convergence
Related Software: Genocop; NOMAD; OrthoMADS; PEET; Matlab; itsmr; Kron; grTheory; Quipper; QuTiP; HeuristicLab; LKH; TSPLIB; PROGRESS
Cited in: 6 Documents