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.
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References in zbMATH (referenced in 4 articles , 1 standard article )
Showing results 1 to 4 of 4.
- Dominique, Stéphane; Trépanier, Jean-Yves; Tribes, Christophe: GATE: A genetic algorithm designed for expensive cost functions (2012)
- Affenzeller, Michael; Winkler, Stephan; Wagner, Stefan; Beham, Andreas: Genetic algorithms and genetic programming. Modern concepts and practical applications. (2009)
- Gen, Mitsuo; Cheng, Runwei; Lin, Lin: Network models and optimization. Multiobjective genetic algorithm approach (2008)
- Dawid, Herbert: Adaptive learning by genetic algorithms. Analytical results and applications to economic models. (1999)