The present paper introduces the GATE algorithm, which was specifically designed to lessen the cost of 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.
['S. Dominique', 'J.-Y. Trepanier', 'Christophe Tribes']
GATE: a genetic algorithm designed for expensive cost functions