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Recombination by decomposition in evolutionary computation


The recombination of solutions is important for evolutionary computation, particularly for genetic algorithms. Recombination is also interesting in other optimization strategies: it can be used to recombine solutions produced in different runs of an algorithm or to recombine solutions produced by different algorithms. The main objective of this project is the investigation of new operators for recombination by decomposition in problems where the evaluation function is composed by a sum of terms. Recombination by decomposition partitions the decision variables of the problem in order to allow the decomposition of the evaluation function. In this way, it allows to find, with computational cost proportional to the cost of evaluating one solution of the problem, the best solution among a number of offspring solutions that grows exponentially with the number of partitions found by the recombination operator. In this project, recombination by decomposition operators will be investigated in combinatorial optimization problems involving graphs and in k-bounded pseudo-Boolean optimization problems. (AU)

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Scientific publications
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
TINOS, RENATO; WHITLEY, DARRELL; OCHOA, GABRIELA. A New Generalized Partition Crossover for the Traveling Salesman Problem: Tunneling between Local Optima. EVOLUTIONARY COMPUTATION, v. 28, n. 2, p. 255-288, . (16/18615-0, 15/06462-1, 13/07375-0)
TINOS, RENATO; YANG, SHENGXIANG. A framework for inducing artificial changes in optimization problems. INFORMATION SCIENCES, v. 485, p. 486-504, . (16/18615-0, 15/06462-1, 13/07375-0)
TINOS, RENATO; ZHAO, LIANG; CHICANO, FRANCISCO; WHITLEY, DARRELL. NK Hybrid Genetic Algorithm for Clustering. IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, v. 22, n. 5, p. 748-761, . (15/06462-1, 15/50122-0, 13/07375-0)

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